{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#调用模块\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#竞赛的评价指标为logloss\n",
    "from sklearn.metrics import log_loss  \n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "dpath = './'\n",
    "train = pd.read_csv(dpath +\"diabetes.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VPHHKSZK0QPW5imn8uRCPAncAawapRpI0NfqMQfhcCElahGZ75Oh7Ztmvqup9A9QjSZoS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\nkjoZEJKkTgaEJKmTASFJ6vT/AUeMG8jkWPD8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeb6e7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Target 分布，看看各类样本分布是否均衡\n",
    "sns.countplot(train.Outcome);\n",
    "pyplot.xlabel('target');\n",
    "pyplot.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y= train['Outcome'].values \n",
    "x= train.drop([ \"Outcome\"], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化,归一化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "\n",
    "ss_x = StandardScaler()\n",
    "ss_x = MinMaxScaler()\n",
    "\n",
    "x = ss_x.fit_transform(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614L, 8L)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=77, test_size=0.2)\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr= LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [ 0.5782106   0.46962048  0.65186035  0.75159113  0.64229015  0.33137976\n",
      "  0.44780944  0.43488434  0.51880261  0.43812255  0.79955182  0.61541176\n",
      "  0.51442874  0.35920118  0.67859652  0.72785324  0.42654127  0.59072727\n",
      "  0.48866326  0.55970705  0.53387363  0.59169264  0.35861694  0.77192851\n",
      "  0.63850849  0.26689325  0.36810343  0.3251341   0.48982087  0.48728249\n",
      "  0.56158638  0.3545431   0.67618542  0.36707593  0.78658694  0.31281686\n",
      "  0.53786742  0.52320206  0.41412586  0.46935094  0.38757768  0.71591128\n",
      "  0.59547628  0.41126519  0.4399403   0.45518738  0.91331765  0.50109056\n",
      "  0.26361259  0.26929994  0.44244338  0.43945816  0.29096975  0.62045743\n",
      "  0.51994428  0.73419974  0.47432756  0.27922974  0.63841116  0.39758223\n",
      "  0.3039778   0.28712164  0.41518182  0.6758453   0.29588772  0.37421133\n",
      "  0.54235501  0.58721393  0.7262237   0.51420448  0.32946783  0.42953838\n",
      "  0.62840449  0.41590376  0.41013452  0.29406093  0.36248471  0.52654359\n",
      "  0.54718775  0.3608689   0.542608    0.72876405  0.58837518  0.58867314\n",
      "  0.55326078  0.57073246  0.542725    0.55851089  0.40231094  0.32173052\n",
      "  0.44994237  0.4224537   0.62890609  0.35230702  0.53180549  0.52433588\n",
      "  0.50763194  0.34120738  0.46827536  0.39884379]\n",
      "cv logloss is: 0.497003686297\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "loss = cross_val_score(lr, x_train, y_train, cv=100, scoring='neg_log_loss')\n",
    "print 'logloss of each fold is: ',-loss\n",
    "print'cv logloss is:', -loss.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=100, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=100,scoring='neg_log_loss')\n",
    "grid.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split10_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split11_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split12_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split13_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split14_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split15_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split16_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split17_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split18_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split19_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split20_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split21_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split22_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split23_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split24_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split25_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split26_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split27_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split28_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split29_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split30_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split31_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split32_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split33_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split34_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split35_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split36_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split37_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split38_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split39_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split40_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split41_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split42_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split43_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split44_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split45_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split46_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split47_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split48_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split49_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split50_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split51_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split52_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split53_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split54_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split55_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split56_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split57_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split58_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split59_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split5_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split60_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split61_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split62_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split63_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split64_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split65_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split66_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split67_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split68_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split69_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split6_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split70_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split71_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split72_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split73_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split74_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split75_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split76_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split77_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split78_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split79_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split7_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split80_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split81_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split82_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split83_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split84_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split85_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split86_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split87_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split88_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split89_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split8_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split90_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split91_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split92_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split93_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split94_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split95_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split96_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split97_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split98_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split99_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split9_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 0.00079   ,  0.00253   ,  0.0011    ,  0.00126   ,  0.00365   ,\n",
       "         0.00204   ,  0.01026999,  0.00208001,  0.01429999,  0.00189   ,\n",
       "         0.0125    ,  0.00233001,  0.01289   ,  0.00186999]),\n",
       " 'mean_score_time': array([ 0.00156   ,  0.00239   ,  0.00097   ,  0.00139   ,  0.00152   ,\n",
       "         0.00075   ,  0.0009    ,  0.00181   ,  0.00153001,  0.00106   ,\n",
       "         0.00108   ,  0.00132   ,  0.0008    ,  0.00084   ]),\n",
       " 'mean_test_score': array([-0.69314718, -0.67986772, -0.69314718, -0.65489013, -0.59036126,\n",
       "        -0.59315179, -0.47451164, -0.49906753, -0.4739027 , -0.47375315,\n",
       "        -0.47418418, -0.47397464, -0.47422085, -0.47419781]),\n",
       " 'mean_train_score': array([-0.69314718, -0.67980613, -0.69314718, -0.65440963, -0.5873312 ,\n",
       "        -0.59026393, -0.46375972, -0.4906642 , -0.4582793 , -0.45985444,\n",
       "        -0.45820196, -0.45822503, -0.45820109, -0.45820132]),\n",
       " 'param_C': masked_array(data = [0.001 0.001 0.01 0.01 0.1 0.1 1 1 10 10 100 100 1000 1000],\n",
       "              mask = [False False False False False False False False False False False False\n",
       "  False False],\n",
       "        fill_value = ?),\n",
       " 'param_penalty': masked_array(data = ['l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2' 'l1' 'l2'],\n",
       "              mask = [False False False False False False False False False False False False\n",
       "  False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'C': 0.001, 'penalty': 'l1'},\n",
       "  {'C': 0.001, 'penalty': 'l2'},\n",
       "  {'C': 0.01, 'penalty': 'l1'},\n",
       "  {'C': 0.01, 'penalty': 'l2'},\n",
       "  {'C': 0.1, 'penalty': 'l1'},\n",
       "  {'C': 0.1, 'penalty': 'l2'},\n",
       "  {'C': 1, 'penalty': 'l1'},\n",
       "  {'C': 1, 'penalty': 'l2'},\n",
       "  {'C': 10, 'penalty': 'l1'},\n",
       "  {'C': 10, 'penalty': 'l2'},\n",
       "  {'C': 100, 'penalty': 'l1'},\n",
       "  {'C': 100, 'penalty': 'l2'},\n",
       "  {'C': 1000, 'penalty': 'l1'},\n",
       "  {'C': 1000, 'penalty': 'l2'}],\n",
       " 'rank_test_score': array([13, 12, 13, 11,  9, 10,  7,  8,  2,  1,  4,  3,  6,  5]),\n",
       " 'split0_test_score': array([-0.69314718, -0.68792852, -0.69314718, -0.68420066, -0.64466935,\n",
       "        -0.65219951, -0.56429616, -0.5782106 , -0.56626228, -0.56163941,\n",
       "        -0.56693519, -0.56612409, -0.56700661, -0.56694217]),\n",
       " 'split0_train_score': array([-0.69314718, -0.67965248, -0.69314718, -0.65396592, -0.58670335,\n",
       "        -0.58946882, -0.46276249, -0.48975466, -0.45720878, -0.45880329,\n",
       "        -0.45713068, -0.4571541 , -0.45712981, -0.45713005]),\n",
       " 'split10_test_score': array([-0.69314718, -0.68616133, -0.69314718, -0.67637178, -0.79365079,\n",
       "        -0.66038741, -1.00595641, -0.79955182, -1.06761625, -1.00541721,\n",
       "        -1.07511593, -1.06725514, -1.07591089, -1.07514129]),\n",
       " 'split10_train_score': array([-0.69314718, -0.67969132, -0.69314718, -0.6540962 , -0.58140281,\n",
       "        -0.58901524, -0.45722814, -0.48588798, -0.45163221, -0.45334636,\n",
       "        -0.45155198, -0.45157746, -0.45155108, -0.45155135]),\n",
       " 'split11_test_score': array([-0.69314718, -0.68804507, -0.69314718, -0.68365884, -0.66440152,\n",
       "        -0.65116816, -0.61875963, -0.61541176, -0.65443231, -0.64235327,\n",
       "        -0.65990674, -0.65806781, -0.66047519, -0.66028182]),\n",
       " 'split11_train_score': array([-0.69314718, -0.67965065, -0.69314718, -0.6539833 , -0.58567185,\n",
       "        -0.5894248 , -0.46192589, -0.48901263, -0.45610183, -0.45772255,\n",
       "        -0.45602354, -0.45604741, -0.45602265, -0.4560229 ]),\n",
       " 'split12_test_score': array([-0.69314718, -0.68709701, -0.69314718, -0.68232169, -0.58608555,\n",
       "        -0.64644548, -0.42927285, -0.51442874, -0.40440643, -0.42238611,\n",
       "        -0.40161963, -0.40368876, -0.40134359, -0.401548  ]),\n",
       " 'split12_train_score': array([-0.69314718, -0.67966887, -0.69314718, -0.65398697, -0.58812943,\n",
       "        -0.58963698, -0.46439877, -0.49090345, -0.459072  , -0.46060663,\n",
       "        -0.45899494, -0.45901733, -0.45899408, -0.45899431]),\n",
       " 'split13_test_score': array([-0.69314718, -0.69005184, -0.69314718, -0.68266995, -0.57114402,\n",
       "        -0.58329353, -0.26985913, -0.35920118, -0.23280229, -0.25265239,\n",
       "        -0.22936967, -0.23169205, -0.22901892, -0.22923313]),\n",
       " 'split13_train_score': array([-0.69314718, -0.67961394, -0.69314718, -0.65408613, -0.58914886,\n",
       "        -0.59091831, -0.46648793, -0.49288169, -0.46103255, -0.46255936,\n",
       "        -0.46095539, -0.46097759, -0.46095454, -0.46095476]),\n",
       " 'split14_test_score': array([-0.69314718, -0.68686272, -0.69314718, -0.68824029, -0.65733828,\n",
       "        -0.71020663, -0.62113523, -0.67859652, -0.59784796, -0.61280628,\n",
       "        -0.59284414, -0.59474001, -0.59234326, -0.59252929]),\n",
       " 'split14_train_score': array([-0.69314718, -0.67967128, -0.69314718, -0.65382143, -0.58558071,\n",
       "        -0.588397  , -0.46205192, -0.48872193, -0.45691151, -0.45843318,\n",
       "        -0.45683514, -0.45685732, -0.45683428, -0.45683451]),\n",
       " 'split15_test_score': array([-0.69314718, -0.68579231, -0.69314718, -0.67811867, -0.71996711,\n",
       "        -0.6658485 , -0.83061765, -0.72785324, -0.91192445, -0.86602085,\n",
       "        -0.92141463, -0.9153074 , -0.92241233, -0.92181624]),\n",
       " 'split15_train_score': array([-0.69314718, -0.67969617, -0.69314718, -0.65403803, -0.58393302,\n",
       "        -0.58900631, -0.45913448, -0.48708084, -0.45316765, -0.45487321,\n",
       "        -0.45308833, -0.45311365, -0.45308741, -0.45308768]),\n",
       " 'split16_test_score': array([-0.69314718, -0.68841479, -0.69314718, -0.6769589 , -0.59432438,\n",
       "        -0.5822451 , -0.37681436, -0.42654127, -0.36316781, -0.36844661,\n",
       "        -0.36187445, -0.36229641, -0.36175532, -0.36180092]),\n",
       " 'split16_train_score': array([-0.69314718, -0.67964683, -0.69314718, -0.65417899, -0.58840274,\n",
       "        -0.5907521 , -0.46487447, -0.49175847, -0.4595002 , -0.46107163,\n",
       "        -0.45942249, -0.45944553, -0.45942162, -0.45942185]),\n",
       " 'split17_test_score': array([-0.69314718, -0.68451048, -0.69314718, -0.66795407, -0.64690148,\n",
       "        -0.612792  , -0.72035169, -0.59072727, -0.78035492, -0.72076003,\n",
       "        -0.78802326, -0.78004199, -0.78882364, -0.78808133]),\n",
       " 'split17_train_score': array([-0.69314718, -0.6797261 , -0.69314718, -0.65425315, -0.58688651,\n",
       "        -0.59000126, -0.46054   , -0.48905535, -0.4549089 , -0.45665527,\n",
       "        -0.4548296 , -0.45485559, -0.45482871, -0.45482897]),\n",
       " 'split18_test_score': array([-0.69314718, -0.68997795, -0.69314718, -0.69120704, -0.60643451,\n",
       "        -0.65701979, -0.35135775, -0.48866326, -0.28935789, -0.32434595,\n",
       "        -0.28138371, -0.28549139, -0.28055408, -0.28093087]),\n",
       " 'split18_train_score': array([-0.69314718, -0.67961108, -0.69314718, -0.65386232, -0.58745714,\n",
       "        -0.58955385, -0.46572942, -0.49162759, -0.46047593, -0.46196994,\n",
       "        -0.46039934, -0.46042113, -0.46039843, -0.46039866]),\n",
       " 'split19_test_score': array([-0.69314718, -0.67698125, -0.69314718, -0.64612676, -0.63037622,\n",
       "        -0.58670693, -0.58981922, -0.55970705, -0.63577149, -0.6157883 ,\n",
       "        -0.64300247, -0.64015575, -0.64371397, -0.64345877]),\n",
       " 'split19_train_score': array([-0.69314718, -0.67985344, -0.69314718, -0.65451369, -0.58607662,\n",
       "        -0.59021198, -0.46244059, -0.48961684, -0.45659389, -0.45822257,\n",
       "        -0.45651519, -0.45653922, -0.45651434, -0.45651457]),\n",
       " 'split1_test_score': array([-0.69314718, -0.68702952, -0.69314718, -0.67305648, -0.64386268,\n",
       "        -0.59155836, -0.45459196, -0.46962048, -0.40727943, -0.41368526,\n",
       "        -0.40153412, -0.40236482, -0.40095725, -0.40104252]),\n",
       " 'split1_train_score': array([-0.69314718, -0.67967596, -0.69314718, -0.65422255, -0.58668434,\n",
       "        -0.59051988, -0.46459012, -0.49122017, -0.45914345, -0.4606864 ,\n",
       "        -0.45906702, -0.45908948, -0.45906617, -0.45906639]),\n",
       " 'split20_test_score': array([-0.69314718, -0.67596757, -0.69314718, -0.64526637, -0.62441836,\n",
       "        -0.59890515, -0.52428843, -0.53387363, -0.49460722, -0.50030327,\n",
       "        -0.49164342, -0.49236915, -0.49134305, -0.49141451]),\n",
       " 'split20_train_score': array([-0.69314718, -0.67987039, -0.69314718, -0.65449604, -0.58596569,\n",
       "        -0.59004488, -0.46346375, -0.49023065, -0.45808111, -0.45962707,\n",
       "        -0.45800459, -0.45802713, -0.4580037 , -0.45800394]),\n",
       " 'split21_test_score': array([-0.69314718, -0.68051941, -0.69314718, -0.65919687, -0.59632065,\n",
       "        -0.62762678, -0.55837755, -0.59169264, -0.5950983 , -0.59207051,\n",
       "        -0.59830996, -0.59767884, -0.59864286, -0.59855506]),\n",
       " 'split21_train_score': array([-0.69314718, -0.67979283, -0.69314718, -0.65432064, -0.58714695,\n",
       "        -0.58968252, -0.46259535, -0.48956307, -0.45705159, -0.45863646,\n",
       "        -0.45697359, -0.45699689, -0.45697274, -0.45697298]),\n",
       " 'split22_test_score': array([-0.69314718, -0.68156926, -0.69314718, -0.65884068, -0.49674239,\n",
       "        -0.57526652, -0.23282186, -0.35861694, -0.21249978, -0.23515986,\n",
       "        -0.21029923, -0.21278682, -0.21008831, -0.21029254]),\n",
       " 'split22_train_score': array([-0.69314718, -0.67977517, -0.69314718, -0.65437361, -0.58984565,\n",
       "        -0.59071879, -0.46631544, -0.49264158, -0.46084019, -0.46237841,\n",
       "        -0.46076345, -0.4607859 , -0.4607626 , -0.46076282]),\n",
       " 'split23_test_score': array([-0.69314718, -0.67609576, -0.69314718, -0.65228267, -0.66082303,\n",
       "        -0.67247802, -0.83055284, -0.77192851, -0.88659181, -0.86383086,\n",
       "        -0.89380699, -0.89098309, -0.8947122 , -0.89447211]),\n",
       " 'split23_train_score': array([-0.69314718, -0.67986649, -0.69314718, -0.65433541, -0.5845438 ,\n",
       "        -0.5887805 , -0.4596945 , -0.48722474, -0.45418219, -0.45580901,\n",
       "        -0.45411012, -0.45413406, -0.4541093 , -0.45410955]),\n",
       " 'split24_test_score': array([-0.69314718, -0.67898668, -0.69314718, -0.65870878, -0.60454532,\n",
       "        -0.65462189, -0.56874009, -0.63850849, -0.60190919, -0.61121943,\n",
       "        -0.60619837, -0.60703534, -0.60664828, -0.60669442]),\n",
       " 'split24_train_score': array([-0.69314718, -0.67981735, -0.69314718, -0.65428361, -0.58625543,\n",
       "        -0.58923176, -0.46256798, -0.48922706, -0.45701485, -0.45858041,\n",
       "        -0.45693682, -0.45695985, -0.45693596, -0.4569362 ]),\n",
       " 'split25_test_score': array([-0.69314718, -0.6773986 , -0.69314718, -0.63752097, -0.51884337,\n",
       "        -0.50291282, -0.1805524 , -0.26689325, -0.13410782, -0.15353051,\n",
       "        -0.12960919, -0.13184333, -0.12918089, -0.12936469]),\n",
       " 'split25_train_score': array([-0.69314718, -0.67985151, -0.69314718, -0.6547105 , -0.58954199,\n",
       "        -0.59167947, -0.46706897, -0.49345952, -0.46162083, -0.46315455,\n",
       "        -0.46154386, -0.46156625, -0.46154302, -0.46154324]),\n",
       " 'split26_test_score': array([-0.69314718, -0.67865127, -0.69314718, -0.64918915, -0.484467  ,\n",
       "        -0.55856231, -0.27234024, -0.36810343, -0.26178843, -0.27706992,\n",
       "        -0.26018476, -0.26185079, -0.26002779, -0.26016433]),\n",
       " 'split26_train_score': array([-0.69314718, -0.67982525, -0.69314718, -0.65450296, -0.59014093,\n",
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       "        -0.46029809, -0.46032062, -0.46029721, -0.46029744]),\n",
       " 'split27_test_score': array([-0.69314718, -0.67791455, -0.69314718, -0.63741554, -0.50861013,\n",
       "        -0.49874983, -0.2931293 , -0.3251341 , -0.28139206, -0.2847907 ,\n",
       "        -0.28044139, -0.28075591, -0.28034706, -0.28037905]),\n",
       " 'split27_train_score': array([-0.69314718, -0.67984353, -0.69314718, -0.65472981, -0.58938135,\n",
       "        -0.59167837, -0.4657664 , -0.49248193, -0.46015512, -0.461719  ,\n",
       "        -0.46007883, -0.46010159, -0.46007795, -0.46007817]),\n",
       " 'split28_test_score': array([-0.69314718, -0.67725332, -0.69314718, -0.64883221, -0.58146363,\n",
       "        -0.59755235, -0.43422627, -0.48982087, -0.42808905, -0.43761009,\n",
       "        -0.42760066, -0.42861157, -0.42755485, -0.42763105]),\n",
       " 'split28_train_score': array([-0.69314718, -0.67984892, -0.69314718, -0.65445509, -0.5869051 ,\n",
       "        -0.59017313, -0.46438128, -0.49086757, -0.45873369, -0.46028639,\n",
       "        -0.45865706, -0.45867973, -0.45865622, -0.45865643]),\n",
       " 'split29_test_score': array([-0.69314718, -0.67764069, -0.69314718, -0.65020154, -0.57552007,\n",
       "        -0.59485762, -0.43558928, -0.48728249, -0.42537359, -0.43644297,\n",
       "        -0.42543231, -0.42665606, -0.42543179, -0.42553767]),\n",
       " 'split29_train_score': array([-0.69314718, -0.67984178, -0.69314718, -0.65444542, -0.58689774,\n",
       "        -0.59024015, -0.46443032, -0.49089065, -0.45876965, -0.46031744,\n",
       "        -0.45869256, -0.45871512, -0.45869169, -0.45869192]),\n",
       " 'split2_test_score': array([-0.69314718, -0.68937605, -0.69314718, -0.68983842, -0.67676175,\n",
       "        -0.67568983, -0.63333291, -0.65186035, -0.66369024, -0.65906224,\n",
       "        -0.66692672, -0.66609317, -0.66726366, -0.66714385]),\n",
       " 'split2_train_score': array([-0.69314718, -0.67962339, -0.69314718, -0.65386645, -0.58479432,\n",
       "        -0.58902768, -0.461768  , -0.48867675, -0.45605367, -0.45764758,\n",
       "        -0.45597553, -0.45599896, -0.45597464, -0.45597489]),\n",
       " 'split30_test_score': array([-0.69314718, -0.67642332, -0.69314718, -0.65199501, -0.55746331,\n",
       "        -0.63232407, -0.54667972, -0.56158638, -0.56704728, -0.55871342,\n",
       "        -0.5701285 , -0.56880443, -0.57044164, -0.57034852]),\n",
       " 'split30_train_score': array([-0.69314718, -0.67986126, -0.69314718, -0.65436885, -0.58753992,\n",
       "        -0.58966562, -0.46279949, -0.48998201, -0.45732856, -0.45893113,\n",
       "        -0.45725069, -0.45727424, -0.45724982, -0.45725005]),\n",
       " 'split31_test_score': array([-0.69314718, -0.67788579, -0.69314718, -0.64264099, -0.51463157,\n",
       "        -0.53135472, -0.28030902, -0.3545431 , -0.25247639, -0.26922441,\n",
       "        -0.24950108, -0.25147569, -0.24919695, -0.24938452]),\n",
       " 'split31_train_score': array([-0.69314718, -0.67984057, -0.69314718, -0.65462291, -0.58996867,\n",
       "        -0.59119829, -0.4657867 , -0.49238788, -0.46044606, -0.46197928,\n",
       "        -0.4603687 , -0.46039103, -0.46036782, -0.46036805]),\n",
       " 'split32_test_score': array([-0.69314718, -0.67633802, -0.69314718, -0.65060699, -0.6159301 ,\n",
       "        -0.64616254, -0.68582467, -0.67618542, -0.71323955, -0.70788317,\n",
       "        -0.71705298, -0.71631555, -0.71744627, -0.71737737]),\n",
       " 'split32_train_score': array([-0.69314718, -0.67986314, -0.69314718, -0.65438366, -0.58545698,\n",
       "        -0.58926081, -0.46137024, -0.48846282, -0.4558869 , -0.45746444,\n",
       "        -0.4558094 , -0.45583246, -0.45580848, -0.45580872]),\n",
       " 'split33_test_score': array([-0.69314718, -0.67896601, -0.69314718, -0.64666093, -0.54223704,\n",
       "        -0.53905444, -0.31029851, -0.36707593, -0.28417707, -0.29497414,\n",
       "        -0.28018738, -0.28145792, -0.27979572, -0.2799098 ]),\n",
       " 'split33_train_score': array([-0.69314718, -0.67982165, -0.69314718, -0.65456322, -0.58841683,\n",
       "        -0.59112213, -0.46543599, -0.49218674, -0.46013678, -0.46168109,\n",
       "        -0.4600601 , -0.46008264, -0.46005926, -0.46005949]),\n",
       " 'split34_test_score': array([-0.69314718, -0.67326864, -0.69314718, -0.64801979, -0.61333163,\n",
       "        -0.69618032, -0.79267486, -0.78658694, -0.82881847, -0.82479661,\n",
       "        -0.83632255, -0.83577529, -0.83726247, -0.837228  ]),\n",
       " 'split34_train_score': array([-0.69314718, -0.67991513, -0.69314718, -0.65434748, -0.58553298,\n",
       "        -0.58842735, -0.46015558, -0.48739243, -0.45482879, -0.45641567,\n",
       "        -0.45475349, -0.45477678, -0.45475263, -0.45475287]),\n",
       " 'split35_test_score': array([-0.69314718, -0.67741356, -0.69314718, -0.6398225 , -0.50038947,\n",
       "        -0.51605069, -0.23579581, -0.31281686, -0.21436618, -0.2270549 ,\n",
       "        -0.21186555, -0.21324687, -0.21161788, -0.21174068]),\n",
       " 'split35_train_score': array([-0.69314718, -0.67984921, -0.69314718, -0.65467051, -0.59034659,\n",
       "        -0.59144628, -0.46621001, -0.49287202, -0.46081618, -0.46236608,\n",
       "        -0.46073929, -0.4607619 , -0.46073843, -0.46073866]),\n",
       " 'split36_test_score': array([-0.69314718, -0.67505443, -0.69314718, -0.64393802, -0.59314064,\n",
       "        -0.60429347, -0.52242015, -0.53786742, -0.51244259, -0.51291174,\n",
       "        -0.51072721, -0.51070439, -0.51055467, -0.51055122]),\n",
       " 'split36_train_score': array([-0.69314718, -0.67988557, -0.69314718, -0.654499  , -0.58683644,\n",
       "        -0.5899612 , -0.46325959, -0.49018345, -0.45786355, -0.45942958,\n",
       "        -0.45778696, -0.45780988, -0.45778609, -0.45778633]),\n",
       " 'split37_test_score': array([-0.69314718, -0.67514492, -0.69314718, -0.63722136, -0.52777993,\n",
       "        -0.54987084, -0.64929428, -0.52320206, -0.78800143, -0.715547  ,\n",
       "        -0.80582277, -0.79597787, -0.80763522, -0.80671569]),\n",
       " 'split37_train_score': array([-0.69314718, -0.67988723, -0.69314718, -0.65465912, -0.58955301,\n",
       "        -0.59079886, -0.46103779, -0.48969073, -0.45521807, -0.45698362,\n",
       "        -0.4551372 , -0.45516359, -0.4551363 , -0.45513658]),\n",
       " 'split38_test_score': array([-0.69314718, -0.67760977, -0.69314718, -0.64632738, -0.54534845,\n",
       "        -0.56242569, -0.36880184, -0.41412586, -0.36000296, -0.36134462,\n",
       "        -0.36023255, -0.36001714, -0.36027076, -0.36025545]),\n",
       " 'split38_train_score': array([-0.69314718, -0.67984311, -0.69314718, -0.65453102, -0.58902484,\n",
       "        -0.59072923, -0.4647873 , -0.49168859, -0.45935609, -0.46094296,\n",
       "        -0.45927788, -0.45930126, -0.45927702, -0.45927726]),\n",
       " 'split39_test_score': array([-0.69314718, -0.67997177, -0.69314718, -0.6554183 , -0.63387364,\n",
       "        -0.5994006 , -0.38296695, -0.46935094, -0.31182373, -0.34019155,\n",
       "        -0.30247836, -0.3059781 , -0.30154063, -0.30186044]),\n",
       " 'split39_train_score': array([-0.69314718, -0.67980267, -0.69314718, -0.65439521, -0.58600678,\n",
       "        -0.59017644, -0.46516865, -0.49138517, -0.45996652, -0.46146698,\n",
       "        -0.45989007, -0.459912  , -0.45988922, -0.45988945]),\n",
       " 'split3_test_score': array([-0.69314718, -0.68730297, -0.69314718, -0.68592349, -0.70802471,\n",
       "        -0.69247977, -0.80981552, -0.75159113, -0.84786837, -0.82470819,\n",
       "        -0.8508908 , -0.84790797, -0.85118694, -0.85092791]),\n",
       " 'split3_train_score': array([-0.69314718, -0.67966334, -0.69314718, -0.65389432, -0.58411423,\n",
       "        -0.58856131, -0.45929573, -0.48704339, -0.45392948, -0.45555648,\n",
       "        -0.45385091, -0.45387489, -0.45385002, -0.45385025]),\n",
       " 'split40_test_score': array([-0.69314718, -0.67714394, -0.69314718, -0.64088353, -0.52761058,\n",
       "        -0.5402935 , -0.34315079, -0.38757768, -0.32908326, -0.33531649,\n",
       "        -0.32749138, -0.32814919, -0.32733934, -0.32740509]),\n",
       " 'split40_train_score': array([-0.69314718, -0.6798541 , -0.69314718, -0.65462611, -0.58922751,\n",
       "        -0.5910309 , -0.46503148, -0.49190237, -0.45967695, -0.46123542,\n",
       "        -0.4596001 , -0.45962286, -0.45959926, -0.45959948]),\n",
       " 'split41_test_score': array([-0.69314718, -0.675263  , -0.69314718, -0.64477652, -0.63295418,\n",
       "        -0.62256367, -0.82063088, -0.71591128, -0.95820041, -0.90305536,\n",
       "        -0.97714043, -0.9695569 , -0.97898868, -0.97835906]),\n",
       " 'split41_train_score': array([-0.69314718, -0.67988213, -0.69314718, -0.6544889 , -0.58509985,\n",
       "        -0.58954563, -0.45973293, -0.48753216, -0.45351577, -0.4552323 ,\n",
       "        -0.45343474, -0.45346031, -0.45343385, -0.4534341 ]),\n",
       " 'split42_test_score': array([-0.69314718, -0.67703969, -0.69314718, -0.65205167, -0.59942383,\n",
       "        -0.63773785, -0.54742656, -0.59547628, -0.54364242, -0.55111027,\n",
       "        -0.54137334, -0.54219649, -0.54115783, -0.54122277]),\n",
       " 'split42_train_score': array([-0.69314718, -0.67985103, -0.69314718, -0.65437373, -0.58657453,\n",
       "        -0.58945633, -0.46289097, -0.4896839 , -0.4575838 , -0.45913589,\n",
       "        -0.45750692, -0.45752966, -0.45750608, -0.45750631]),\n",
       " 'split43_test_score': array([-0.69314718, -0.67414713, -0.69314718, -0.63259576, -0.66124442,\n",
       "        -0.54100548, -0.40329053, -0.41126519, -0.33408181, -0.34468832,\n",
       "        -0.3279636 , -0.32933192, -0.32734415, -0.32748244]),\n",
       " 'split43_train_score': array([-0.69314718, -0.67990653, -0.69314718, -0.65472482, -0.58522429,\n",
       "        -0.59094925, -0.46526442, -0.49171038, -0.45972997, -0.46126358,\n",
       "        -0.45965276, -0.45967511, -0.45965188, -0.45965212]),\n",
       " 'split44_test_score': array([-0.69314718, -0.6776152 , -0.69314718, -0.64395796, -0.61179234,\n",
       "        -0.55804285, -0.4027791 , -0.4399403 , -0.3746336 , -0.38256649,\n",
       "        -0.37150744, -0.37235566, -0.37119872, -0.37126112]),\n",
       " 'split44_train_score': array([-0.69314718, -0.6798449 , -0.69314718, -0.65457822, -0.58709928,\n",
       "        -0.59071807, -0.46462814, -0.49135947, -0.45925497, -0.46080836,\n",
       "        -0.45917803, -0.45920076, -0.45917716, -0.45917739]),\n",
       " 'split45_test_score': array([-0.69314718, -0.6777463 , -0.69314718, -0.6456841 , -0.57178091,\n",
       "        -0.5659379 , -0.41794183, -0.45518738, -0.42823432, -0.43032981,\n",
       "        -0.43140602, -0.43140805, -0.43173708, -0.43172653]),\n",
       " 'split45_train_score': array([-0.69314718, -0.67984188, -0.69314718, -0.65454214, -0.58791154,\n",
       "        -0.59061631, -0.46428211, -0.49110132, -0.45870093, -0.460275  ,\n",
       "        -0.45862319, -0.45864621, -0.45862232, -0.45862255]),\n",
       " 'split46_test_score': array([-0.69314718, -0.67483519, -0.69314718, -0.64715675, -0.79537965,\n",
       "        -0.67588141, -1.15365451, -0.91331765, -1.2364508 , -1.16674722,\n",
       "        -1.24734047, -1.23857228, -1.24845634, -1.24764012]),\n",
       " 'split46_train_score': array([-0.69314718, -0.67988979, -0.69314718, -0.65441004, -0.58083689,\n",
       "        -0.58857311, -0.45641945, -0.48504788, -0.45086071, -0.45256505,\n",
       "        -0.45078098, -0.45080627, -0.45078009, -0.45078035]),\n",
       " 'split47_test_score': array([-0.69314718, -0.6744935 , -0.69314718, -0.64091759, -0.60785394,\n",
       "        -0.5892153 , -0.49999406, -0.50109056, -0.49999373, -0.49047117,\n",
       "        -0.49765539, -0.49629198, -0.49732918, -0.49722343]),\n",
       " 'split47_train_score': array([-0.69314718, -0.67989689, -0.69314718, -0.65455225, -0.58681197,\n",
       "        -0.59025757, -0.46337892, -0.49060596, -0.45807563, -0.45966563,\n",
       "        -0.45800123, -0.45802446, -0.45800036, -0.45800059]),\n",
       " 'split48_test_score': array([-0.69314718, -0.67778605, -0.69314718, -0.63475794, -0.54121302,\n",
       "        -0.47030399, -0.21473953, -0.26361259, -0.17749693, -0.19016415,\n",
       "        -0.17397201, -0.17546823, -0.17360401, -0.17373701]),\n",
       " 'split48_train_score': array([-0.69314718, -0.67984555, -0.69314718, -0.65480438, -0.58910804,\n",
       "        -0.59212285, -0.46664199, -0.49323697, -0.46118742, -0.46272674,\n",
       "        -0.46111091, -0.46113329, -0.46111002, -0.46111025]),\n",
       " 'split49_test_score': array([-0.69314718, -0.67904873, -0.69314718, -0.64127215, -0.53118376,\n",
       "        -0.4949408 , -0.18980066, -0.26929994, -0.15425445, -0.17273871,\n",
       "        -0.15148465, -0.1535991 , -0.15118009, -0.15137175]),\n",
       " 'split49_train_score': array([-0.69314718, -0.6798229 , -0.69314718, -0.65468756, -0.58969193,\n",
       "        -0.59182355, -0.46694932, -0.49333536, -0.46141587, -0.46295077,\n",
       "        -0.46133972, -0.461362  , -0.46133881, -0.46133904]),\n",
       " 'split4_test_score': array([-0.69314718, -0.68712773, -0.69314718, -0.67976281, -0.625098  ,\n",
       "        -0.63885667, -0.7245437 , -0.64229015, -0.77435165, -0.74016371,\n",
       "        -0.78076732, -0.77632169, -0.78143097, -0.78104592]),\n",
       " 'split4_train_score': array([-0.69314718, -0.6796696 , -0.69314718, -0.65406003, -0.58732219,\n",
       "        -0.58960803, -0.46046129, -0.4884137 , -0.45489111, -0.45655386,\n",
       "        -0.45481196, -0.45483647, -0.45481107, -0.45481132]),\n",
       " 'split50_test_score': array([-0.69314718, -0.67790858, -0.69314718, -0.64640174, -0.5664386 ,\n",
       "        -0.55320638, -0.45693629, -0.44244338, -0.48316421, -0.46146489,\n",
       "        -0.48700323, -0.48388771, -0.48740906, -0.48714091]),\n",
       " 'split50_train_score': array([-0.69314718, -0.67983703, -0.69314718, -0.65455373, -0.58799191,\n",
       "        -0.59082552, -0.46360747, -0.49110749, -0.45811945, -0.45975953,\n",
       "        -0.45804117, -0.45806537, -0.45804026, -0.4580405 ]),\n",
       " 'split51_test_score': array([-0.69314718, -0.6770434 , -0.69314718, -0.6465851 , -0.57660211,\n",
       "        -0.58069422, -0.39268143, -0.43945816, -0.38137739, -0.38537469,\n",
       "        -0.38167489, -0.38189545, -0.38171443, -0.38173441]),\n",
       " 'split51_train_score': array([-0.69314718, -0.67985277, -0.69314718, -0.65449805, -0.58750059,\n",
       "        -0.59043669, -0.46467073, -0.49143512, -0.45913119, -0.4607063 ,\n",
       "        -0.45905367, -0.45907675, -0.45905281, -0.45905305]),\n",
       " 'split52_test_score': array([-0.69314718, -0.67900482, -0.69314718, -0.64431089, -0.49512515,\n",
       "        -0.51241061, -0.2115555 , -0.29096975, -0.18950679, -0.20534552,\n",
       "        -0.18846096, -0.19022368, -0.18833741, -0.18850108]),\n",
       " 'split52_train_score': array([-0.69314718, -0.67982189, -0.69314718, -0.65461856, -0.59000564,\n",
       "        -0.59159327, -0.46657281, -0.49308962, -0.46106048, -0.46260316,\n",
       "        -0.46098393, -0.46100632, -0.460983  , -0.46098323]),\n",
       " 'split53_test_score': array([-0.69314718, -0.67719234, -0.69314718, -0.65047856, -0.57691379,\n",
       "        -0.62133519, -0.63929092, -0.62045743, -0.72638391, -0.70123278,\n",
       "        -0.73782389, -0.7342301 , -0.73898503, -0.73863247]),\n",
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       "        -0.45565663, -0.45568093, -0.45565576, -0.45565601]),\n",
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       "        -0.58249043, -0.5792876 , -0.58306659, -0.58276366]),\n",
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       "        -0.45711277, -0.45713705, -0.45711189, -0.45711215]),\n",
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       "        -0.45497982, -0.45500356, -0.45497895, -0.4549792 ]),\n",
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       "        -0.44422296, -0.44442384, -0.44446341, -0.44445039]),\n",
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       "        -0.4585102 , -0.45853318, -0.45850935, -0.45850957]),\n",
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       "        -0.11903466, -0.12159751, -0.1185836 , -0.11880291]),\n",
       " 'split57_train_score': array([-0.69314718, -0.67981136, -0.69314718, -0.6545716 , -0.58860154,\n",
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       "        -0.67165389, -0.6705445 , -0.67172802, -0.6716006 ]),\n",
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       "        -0.45624307, -0.45626622, -0.45624217, -0.45624241]),\n",
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       "        -0.35544832, -0.35564547, -0.35542891, -0.35544816]),\n",
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       "        -0.45932497, -0.45934784, -0.4593241 , -0.45932432]),\n",
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       "        -0.20230684, -0.20451366, -0.20195026, -0.20213444]),\n",
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       "        -0.4612623 , -0.4612846 , -0.46126145, -0.46126167]),\n",
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       "        -0.20610419, -0.20758538, -0.20574387, -0.20586746]),\n",
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       "        -0.46083844, -0.46086092, -0.46083758, -0.4608378 ]),\n",
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       "        -0.11465313, -0.11723718, -0.11417372, -0.11440286]),\n",
       " 'split61_train_score': array([-0.69314718, -0.67981118, -0.69314718, -0.6545233 , -0.58993823,\n",
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       "        -0.46168992, -0.46171233, -0.46168902, -0.46168925]),\n",
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       "        -0.35768318, -0.35810042, -0.35747156, -0.35752403]),\n",
       " 'split62_train_score': array([-0.69314718, -0.67986448, -0.69314718, -0.65458132, -0.5877749 ,\n",
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       "        -0.45930664, -0.45932948, -0.45930578, -0.45930601]),\n",
       " 'split63_test_score': array([-0.69314718, -0.67429328, -0.69314718, -0.64220982, -0.62093527,\n",
       "        -0.63490268, -0.74890615, -0.6758453 , -0.76447268, -0.74192493,\n",
       "        -0.76601819, -0.76323484, -0.76617434, -0.76596233]),\n",
       " 'split63_train_score': array([-0.69314718, -0.67990109, -0.69314718, -0.65449421, -0.58585157,\n",
       "        -0.58936981, -0.46081453, -0.48836865, -0.45545697, -0.4570639 ,\n",
       "        -0.45537956, -0.45540314, -0.4553787 , -0.45537895]),\n",
       " 'split64_test_score': array([-0.69314718, -0.67752273, -0.69314718, -0.64028961, -0.53347956,\n",
       "        -0.51863551, -0.20284016, -0.29588772, -0.15448853, -0.1752245 ,\n",
       "        -0.14969465, -0.15206088, -0.14920309, -0.14942389]),\n",
       " 'split64_train_score': array([-0.69314718, -0.67984744, -0.69314718, -0.65465979, -0.58917225,\n",
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       "        -0.4613476 , -0.46136995, -0.46134671, -0.46134694]),\n",
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       "        -0.52997606, -0.34205114, -0.37421133, -0.32130655, -0.32528253,\n",
       "        -0.31876705, -0.31916337, -0.31851492, -0.31854451]),\n",
       " 'split65_train_score': array([-0.69314718, -0.67986293, -0.69314718, -0.6546701 , -0.58902259,\n",
       "        -0.591172  , -0.46516664, -0.49202082, -0.45976496, -0.46132449,\n",
       "        -0.45968763, -0.45971037, -0.45968673, -0.45968696]),\n",
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       "        -0.4786696 , -0.47980378, -0.47815218, -0.47826967]),\n",
       " 'split66_train_score': array([-0.69314718, -0.67989858, -0.69314718, -0.65454021, -0.58520608,\n",
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       "        -0.45814504, -0.45816748, -0.4581442 , -0.45814443]),\n",
       " 'split67_test_score': array([-0.69314718, -0.68026357, -0.69314718, -0.66141972, -0.55224854,\n",
       "        -0.64073434, -0.53196221, -0.58721393, -0.59064043, -0.59069219,\n",
       "        -0.59917367, -0.59879682, -0.60005475, -0.59998469]),\n",
       " 'split67_train_score': array([-0.69314718, -0.67979652, -0.69314718, -0.65426742, -0.58739404,\n",
       "        -0.58957683, -0.46290787, -0.4897045 , -0.45714766, -0.45873251,\n",
       "        -0.45706873, -0.457092  , -0.45706786, -0.4570681 ]),\n",
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       "        -0.64569983, -0.78420416, -0.7262237 , -0.86002942, -0.83236706,\n",
       "        -0.86995842, -0.86616474, -0.87096614, -0.87058318]),\n",
       " 'split68_train_score': array([-0.69314718, -0.67982836, -0.69314718, -0.6543544 , -0.58430103,\n",
       "        -0.5892417 , -0.46015706, -0.48771089, -0.45454857, -0.45618192,\n",
       "        -0.45446889, -0.45449306, -0.45446801, -0.45446826]),\n",
       " 'split69_test_score': array([-0.69314718, -0.67699742, -0.69314718, -0.64648101, -0.6106474 ,\n",
       "        -0.57835124, -0.52946925, -0.51420448, -0.52189244, -0.51937963,\n",
       "        -0.52252949, -0.52218891, -0.52258557, -0.52254812]),\n",
       " 'split69_train_score': array([-0.69314718, -0.67985265, -0.69314718, -0.65451135, -0.5858859 ,\n",
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       "        -0.45776808, -0.45779102, -0.45776724, -0.45776748]),\n",
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       "        -0.5952341 , -0.35985807, -0.44780944, -0.33722025, -0.35504782,\n",
       "        -0.33283043, -0.33488562, -0.33239967, -0.33255046]),\n",
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       "        -0.45983529, -0.45985766, -0.45983444, -0.45983467]),\n",
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       "        -0.53581364, -0.24852561, -0.32946783, -0.23582146, -0.24981264,\n",
       "        -0.23530739, -0.23682337, -0.23526341, -0.23538319]),\n",
       " 'split70_train_score': array([-0.69314718, -0.6798157 , -0.69314718, -0.65453949, -0.59013623,\n",
       "        -0.59126062, -0.46614636, -0.49268156, -0.46058912, -0.46213852,\n",
       "        -0.46051254, -0.4605351 , -0.46051168, -0.46051191]),\n",
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       "        -0.5734581 , -0.37314962, -0.42953838, -0.33366036, -0.3494727 ,\n",
       "        -0.33040874, -0.33228056, -0.33009838, -0.33025024]),\n",
       " 'split71_train_score': array([-0.69314718, -0.67985598, -0.69314718, -0.65451682, -0.58671953,\n",
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       "        -0.459581  , -0.45960335, -0.45958016, -0.45958038]),\n",
       " 'split72_test_score': array([-0.69314718, -0.67615425, -0.69314718, -0.65153094, -0.64218172,\n",
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       "        -0.59192671, -0.59233397, -0.59148736, -0.59152117]),\n",
       " 'split72_train_score': array([-0.69314718, -0.67986485, -0.69314718, -0.65436686, -0.58477748,\n",
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       "        -0.45707223, -0.45709493, -0.45707137, -0.4570716 ]),\n",
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       "        -0.31625592, -0.31799145, -0.31581736, -0.31597026]),\n",
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       "        -0.59073806, -0.46503522, -0.49172424, -0.4597974 , -0.46133237,\n",
       "        -0.4597207 , -0.45974308, -0.45971985, -0.45972007]),\n",
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       "        -0.44160501, -0.43901668, -0.44191229, -0.44166815]),\n",
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       "        -0.59089937, -0.46423449, -0.49153043, -0.45862797, -0.4602516 ,\n",
       "        -0.45855044, -0.45857431, -0.45854956, -0.45854981]),\n",
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       "        -0.17705232, -0.17891457, -0.17667844, -0.17685247]),\n",
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       "        -0.46107412, -0.46109646, -0.46107318, -0.46107341]),\n",
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       "        -0.5595557 , -0.25618913, -0.36248471, -0.23789993, -0.25734083,\n",
       "        -0.23576363, -0.23793878, -0.23554963, -0.23574385]),\n",
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       "        -0.46051773, -0.4605402 , -0.46051687, -0.4605171 ]),\n",
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       "        -0.56078166, -0.55923112, -0.56094285, -0.56080749]),\n",
       " 'split77_train_score': array([-0.69314718, -0.67986514, -0.69314718, -0.654564  , -0.5865681 ,\n",
       "        -0.59035399, -0.46284635, -0.49007782, -0.4573779 , -0.45897308,\n",
       "        -0.45729998, -0.45732336, -0.45729909, -0.45729933]),\n",
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       "        -0.602682  , -0.53055293, -0.54718775, -0.50923097, -0.51578639,\n",
       "        -0.50724091, -0.50806217, -0.50704741, -0.50712204]),\n",
       " 'split78_train_score': array([-0.69314718, -0.67988248, -0.69314718, -0.65450999, -0.58601747,\n",
       "        -0.58996377, -0.46333537, -0.49007815, -0.45790954, -0.45945662,\n",
       "        -0.45783316, -0.45785574, -0.45783232, -0.45783254]),\n",
       " 'split79_test_score': array([-0.69314718, -0.67734257, -0.69314718, -0.64395784, -0.55380207,\n",
       "        -0.55228455, -0.30905549, -0.3608689 , -0.26289158, -0.27309187,\n",
       "        -0.25757117, -0.2587049 , -0.257043  , -0.25715263]),\n",
       " 'split79_train_score': array([-0.69314718, -0.67984982, -0.69314718, -0.65456427, -0.58802125,\n",
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       "        -0.46029728, -0.46031993, -0.46029643, -0.46029666]),\n",
       " 'split7_test_score': array([-0.69314718, -0.69190125, -0.69314718, -0.68927117, -0.59183441,\n",
       "        -0.60888671, -0.34660555, -0.43488434, -0.32315411, -0.33892148,\n",
       "        -0.32014346, -0.32184414, -0.31985126, -0.31998485]),\n",
       " 'split7_train_score': array([-0.69314718, -0.67957647, -0.69314718, -0.65397933, -0.58839829,\n",
       "        -0.59039726, -0.46538628, -0.49189361, -0.4599959 , -0.46154141,\n",
       "        -0.45991863, -0.45994129, -0.45991777, -0.459918  ]),\n",
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       "        -0.58134546, -0.60889534, -0.542608  , -0.60604842, -0.58528372,\n",
       "        -0.60615878, -0.60351806, -0.60616557, -0.60595572]),\n",
       " 'split80_train_score': array([-0.69314718, -0.67985916, -0.69314718, -0.65454858, -0.58562093,\n",
       "        -0.59035449, -0.46259612, -0.49000445, -0.45719877, -0.45880284,\n",
       "        -0.45712161, -0.45714513, -0.45712074, -0.45712098]),\n",
       " 'split81_test_score': array([-0.69314718, -0.67571183, -0.69314718, -0.65319949, -0.66422233,\n",
       "        -0.6764651 , -0.73648365, -0.72876405, -0.75618692, -0.75191046,\n",
       "        -0.75870324, -0.75807287, -0.75896422, -0.75889429]),\n",
       " 'split81_train_score': array([-0.69314718, -0.67987165, -0.69314718, -0.654311  , -0.58426206,\n",
       "        -0.58877618, -0.46101096, -0.48794993, -0.45544126, -0.45701836,\n",
       "        -0.45536361, -0.45538676, -0.45536276, -0.455363  ]),\n",
       " 'split82_test_score': array([-0.69314718, -0.67709298, -0.69314718, -0.64952618, -0.58387895,\n",
       "        -0.61685197, -0.58179951, -0.58837518, -0.58763338, -0.58499512,\n",
       "        -0.58822187, -0.58779307, -0.5882962 , -0.58826452]),\n",
       " 'split82_train_score': array([-0.69314718, -0.67985049, -0.69314718, -0.65443418, -0.58680819,\n",
       "        -0.58973711, -0.46247503, -0.48954321, -0.45710513, -0.45868019,\n",
       "        -0.45702765, -0.45705068, -0.45702672, -0.45702696]),\n",
       " 'split83_test_score': array([-0.69314718, -0.67693213, -0.69314718, -0.64742468, -0.64152903,\n",
       "        -0.6069293 , -0.60569932, -0.58867314, -0.61624782, -0.60951672,\n",
       "        -0.61854   , -0.61762237, -0.61877224, -0.61868297]),\n",
       " 'split83_train_score': array([-0.69314718, -0.67985454, -0.69314718, -0.65447391, -0.58594024,\n",
       "        -0.58987979, -0.46230332, -0.48941458, -0.45681587, -0.45839636,\n",
       "        -0.45673781, -0.45676096, -0.45673694, -0.45673718]),\n",
       " 'split84_test_score': array([-0.69314718, -0.67481746, -0.69314718, -0.6420597 , -0.57070567,\n",
       "        -0.60085966, -0.51864924, -0.55326078, -0.53477612, -0.53703672,\n",
       "        -0.53704686, -0.53718988, -0.5372809 , -0.5373251 ]),\n",
       " 'split84_train_score': array([-0.69314718, -0.67989031, -0.69314718, -0.65454096, -0.5870927 ,\n",
       "        -0.5899835 , -0.46318572, -0.49011398, -0.45776545, -0.45933268,\n",
       "        -0.45768712, -0.45771009, -0.45768624, -0.45768648]),\n",
       " 'split85_test_score': array([-0.69314718, -0.67792399, -0.69314718, -0.65612564, -0.5974418 ,\n",
       "        -0.64084032, -0.50579819, -0.57073246, -0.50323501, -0.51517626,\n",
       "        -0.50271704, -0.50400176, -0.50265891, -0.50276352]),\n",
       " 'split85_train_score': array([-0.69314718, -0.67983496, -0.69314718, -0.6543149 , -0.58654646,\n",
       "        -0.58950014, -0.46342053, -0.48999573, -0.45794432, -0.45949306,\n",
       "        -0.45786754, -0.4578902 , -0.45786667, -0.45786691]),\n",
       " 'split86_test_score': array([-0.69314718, -0.67619131, -0.69314718, -0.64527525, -0.61855593,\n",
       "        -0.59659748, -0.53161836, -0.542725  , -0.53790729, -0.53540163,\n",
       "        -0.53828268, -0.53782814, -0.53831875, -0.53825885]),\n",
       " 'split86_train_score': array([-0.69314718, -0.67986689, -0.69314718, -0.65450282, -0.58640388,\n",
       "        -0.5900722 , -0.46312591, -0.49004748, -0.45761088, -0.45918654,\n",
       "        -0.45753383, -0.45755688, -0.45753293, -0.45753318]),\n",
       " 'split87_test_score': array([-0.69314718, -0.6750709 , -0.69314718, -0.64245143, -0.59821758,\n",
       "        -0.6030448 , -0.55820172, -0.55851089, -0.56382641, -0.55962353,\n",
       "        -0.56575894, -0.56510242, -0.56595287, -0.56591596]),\n",
       " 'split87_train_score': array([-0.69314718, -0.67988689, -0.69314718, -0.65452836, -0.58618173,\n",
       "        -0.58995611, -0.4629491 , -0.48991835, -0.45740924, -0.45898953,\n",
       "        -0.4573323 , -0.45735542, -0.45733143, -0.45733166]),\n",
       " 'split88_test_score': array([-0.69314718, -0.67546757, -0.69314718, -0.64014168, -0.53563562,\n",
       "        -0.56623708, -0.33373513, -0.40231094, -0.27714225, -0.29718429,\n",
       "        -0.27095115, -0.27340125, -0.27033462, -0.27056985]),\n",
       " 'split88_train_score': array([-0.69314718, -0.67988181, -0.69314718, -0.6545907 , -0.58852914,\n",
       "        -0.59065086, -0.46557993, -0.49201132, -0.46025435, -0.46177658,\n",
       "        -0.46017757, -0.46019971, -0.46017669, -0.46017691]),\n",
       " 'split89_test_score': array([-0.69314718, -0.67720285, -0.69314718, -0.63797925, -0.54057046,\n",
       "        -0.51046169, -0.26257939, -0.32173052, -0.2208414 , -0.23598011,\n",
       "        -0.21690106, -0.21868438, -0.21650156, -0.21667778]),\n",
       " 'split89_train_score': array([-0.69314718, -0.67985388, -0.69314718, -0.65470297, -0.58900446,\n",
       "        -0.59150941, -0.46618875, -0.4927342 , -0.46079181, -0.46232915,\n",
       "        -0.4607156 , -0.46073802, -0.46071476, -0.46071499]),\n",
       " 'split8_test_score': array([-0.69314718, -0.69154312, -0.69314718, -0.693451  , -0.5948002 ,\n",
       "        -0.65147591, -0.4472619 , -0.51880261, -0.44081529, -0.45026336,\n",
       "        -0.43923313, -0.4402058 , -0.43908488, -0.43916642]),\n",
       " 'split8_train_score': array([-0.69314718, -0.67958183, -0.69314718, -0.65384972, -0.58799776,\n",
       "        -0.58964254, -0.46392062, -0.4907544 , -0.4586197 , -0.46017888,\n",
       "        -0.45854272, -0.45856551, -0.45854184, -0.45854206]),\n",
       " 'split90_test_score': array([-0.69314718, -0.67860316, -0.69314718, -0.65440003, -0.52693012,\n",
       "        -0.60570109, -0.33759407, -0.44994237, -0.30414751, -0.3309497 ,\n",
       "        -0.30042342, -0.30358482, -0.30004829, -0.30033373]),\n",
       " 'split90_train_score': array([-0.69314718, -0.67982449, -0.69314718, -0.6543777 , -0.58864728,\n",
       "        -0.59013118, -0.46534328, -0.49158819, -0.45995097, -0.46146764,\n",
       "        -0.45987487, -0.45989698, -0.45987402, -0.45987425]),\n",
       " 'split91_test_score': array([-0.69314718, -0.67831708, -0.69314718, -0.64971736, -0.54916869,\n",
       "        -0.58211269, -0.33384095, -0.4224537 , -0.30815652, -0.32568729,\n",
       "        -0.3049745 , -0.30695066, -0.30464248, -0.30482023]),\n",
       " 'split91_train_score': array([-0.69314718, -0.67983157, -0.69314718, -0.65446636, -0.58793183,\n",
       "        -0.59045557, -0.46532343, -0.49178051, -0.45989829, -0.46143672,\n",
       "        -0.45982173, -0.45984421, -0.45982085, -0.45982109]),\n",
       " 'split92_test_score': array([-0.69314718, -0.67728305, -0.69314718, -0.64922765, -0.6276272 ,\n",
       "        -0.62028286, -0.64098966, -0.62890609, -0.6564673 , -0.65020028,\n",
       "        -0.65814391, -0.65729169, -0.65833036, -0.65824591]),\n",
       " 'split92_train_score': array([-0.69314718, -0.67984822, -0.69314718, -0.65444247, -0.58592155,\n",
       "        -0.58963681, -0.4618552 , -0.48898239, -0.45644512, -0.45802376,\n",
       "        -0.45636682, -0.45638997, -0.45636593, -0.45636617]),\n",
       " 'split93_test_score': array([-0.69314718, -0.67638664, -0.69314718, -0.64200389, -0.52743854,\n",
       "        -0.55387684, -0.25278563, -0.35230702, -0.20367509, -0.22687004,\n",
       "        -0.19884024, -0.2015179 , -0.19836701, -0.19860123]),\n",
       " 'split93_train_score': array([-0.69314718, -0.67986517, -0.69314718, -0.65458372, -0.58911836,\n",
       "        -0.59088173, -0.46634175, -0.49266185, -0.46094496, -0.46247215,\n",
       "        -0.46086797, -0.46089021, -0.46086709, -0.46086732]),\n",
       " 'split94_test_score': array([-0.69314718, -0.67679853, -0.69314718, -0.64401866, -0.56947002,\n",
       "        -0.57824707, -0.5804838 , -0.53180549, -0.6581539 , -0.62005261,\n",
       "        -0.66912845, -0.66372869, -0.67026896, -0.66974775]),\n",
       " 'split94_train_score': array([-0.69314718, -0.67985833, -0.69314718, -0.65454511, -0.58825835,\n",
       "        -0.59037205, -0.46212915, -0.48990128, -0.45640488, -0.45809015,\n",
       "        -0.45632481, -0.45634984, -0.45632392, -0.45632418]),\n",
       " 'split95_test_score': array([-0.69314718, -0.68127177, -0.69314718, -0.66395834, -0.60213681,\n",
       "        -0.62869613, -0.49835906, -0.52433588, -0.48733552, -0.48844877,\n",
       "        -0.4889241 , -0.48877332, -0.48911474, -0.48910613]),\n",
       " 'split95_train_score': array([-0.69314718, -0.67976733, -0.69314718, -0.65425371, -0.58681606,\n",
       "        -0.58982648, -0.46380389, -0.49050414, -0.45817219, -0.45974838,\n",
       "        -0.45809384, -0.45811696, -0.45809296, -0.4580932 ]),\n",
       " 'split96_test_score': array([-0.69314718, -0.68330866, -0.69314718, -0.66605235, -0.62210873,\n",
       "        -0.60700889, -0.47558251, -0.50763194, -0.44960262, -0.45725236,\n",
       "        -0.44662199, -0.44749076, -0.44631223, -0.44639517]),\n",
       " 'split96_train_score': array([-0.69314718, -0.67973832, -0.69314718, -0.6542693 , -0.58665345,\n",
       "        -0.59005146, -0.46383649, -0.49058815, -0.45847406, -0.46002404,\n",
       "        -0.45839806, -0.45842059, -0.45839708, -0.45839732]),\n",
       " 'split97_test_score': array([-0.69314718, -0.68891952, -0.69314718, -0.67473816, -0.47439917,\n",
       "        -0.54966844, -0.24335999, -0.34120738, -0.24102614, -0.25609067,\n",
       "        -0.24030569, -0.2419122 , -0.24025113, -0.24034621]),\n",
       " 'split97_train_score': array([-0.69314718, -0.67966064, -0.69314718, -0.65426229, -0.59049009,\n",
       "        -0.59095797, -0.46555043, -0.49222781, -0.46018112, -0.4617294 ,\n",
       "        -0.4601048 , -0.46012734, -0.46010391, -0.46010412]),\n",
       " 'split98_test_score': array([-0.69314718, -0.6846083 , -0.69314718, -0.66516547, -0.6589093 ,\n",
       "        -0.57704131, -0.44287642, -0.46827536, -0.42764799, -0.43223907,\n",
       "        -0.42593831, -0.42640476, -0.42576184, -0.42578484]),\n",
       " 'split98_train_score': array([-0.69314718, -0.67972153, -0.69314718, -0.65432264, -0.58633233,\n",
       "        -0.59044535, -0.46428469, -0.49088414, -0.4586769 , -0.46023232,\n",
       "        -0.45860023, -0.45862293, -0.45859935, -0.45859958]),\n",
       " 'split99_test_score': array([-0.69314718, -0.68455448, -0.69314718, -0.6688954 , -0.57858796,\n",
       "        -0.59373644, -0.31914915, -0.39884379, -0.27449897, -0.29182178,\n",
       "        -0.27069547, -0.27264962, -0.27031862, -0.27049241]),\n",
       " 'split99_train_score': array([-0.69314718, -0.67972109, -0.69314718, -0.65424783, -0.58734219,\n",
       "        -0.59035201, -0.46541008, -0.49183874, -0.45992266, -0.46146379,\n",
       "        -0.45984607, -0.45986854, -0.45984521, -0.45984543]),\n",
       " 'split9_test_score': array([-0.69314718, -0.68748982, -0.69314718, -0.67607185, -0.61282969,\n",
       "        -0.59192535, -0.40005692, -0.43812255, -0.38238415, -0.38725695,\n",
       "        -0.38195726, -0.38237882, -0.38192625, -0.38198219]),\n",
       " 'split9_train_score': array([-0.69314718, -0.67966487, -0.69314718, -0.65416247, -0.58774331,\n",
       "        -0.59060349, -0.46488946, -0.49162388, -0.45932479, -0.46089364,\n",
       "        -0.45924723, -0.45927018, -0.45924635, -0.45924659]),\n",
       " 'std_fit_time': array([ 0.0034447 ,  0.01031355,  0.00393573,  0.00427462,  0.00631565,\n",
       "         0.00528001,  0.00882366,  0.00221216,  0.0026702 ,  0.00474741,\n",
       "         0.00653988,  0.00544804,  0.0058136 ,  0.0048655 ]),\n",
       " 'std_score_time': array([ 0.00468257,  0.00561763,  0.00375888,  0.00442243,  0.00453759,\n",
       "         0.00326919,  0.00343657,  0.00273018,  0.00193627,  0.00371435,\n",
       "         0.00393873,  0.00419496,  0.00335857,  0.00339624]),\n",
       " 'std_test_score': array([ 0.        ,  0.00492625,  0.        ,  0.01598807,  0.06120999,\n",
       "         0.05145222,  0.19515265,  0.14049128,  0.22780712,  0.20973675,\n",
       "         0.23185025,  0.22953291,  0.23226905,  0.2320566 ]),\n",
       " 'std_train_score': array([  0.00000000e+00,   8.64543082e-05,   0.00000000e+00,\n",
       "          2.23159024e-04,   1.87812321e-03,   8.42845954e-04,\n",
       "          2.24382158e-03,   1.81213931e-03,   2.30908570e-03,\n",
       "          2.26794132e-03,   2.30974716e-03,   2.30902404e-03,\n",
       "          2.30975145e-03,   2.30974207e-03])}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.473753145274\n",
      "{'penalty': 'l2', 'C': 10}\n"
     ]
    }
   ],
   "source": [
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SSpVGlm6EG2N+A34TkSARqWiM2QP8272lKaWUKm2sDiMSJiIbgFhgi4j8KSJ6T0MppS4x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\npZRlGhpKKaUs09BQSillmYaGUkopy/4fnq3K51g1IrsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xebc1be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    pyplot.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    pyplot.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'neg-logloss' )\n",
    "pyplot.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=[1, 10, 100, 1000], class_weight=None, cv=100,\n",
       "           dual=False, fit_intercept=True, intercept_scaling=1.0,\n",
       "           max_iter=100, multi_class='ovr', n_jobs=1, penalty='l1',\n",
       "           random_state=None, refit=True, scoring='neg_log_loss',\n",
       "           solver='liblinear', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#L1正则\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "Cs = [1, 10,100,1000]\n",
    "\n",
    "\n",
    "lrcv_L1 = LogisticRegressionCV(Cs=Cs, cv = 100, scoring='neg_log_loss', penalty='l1', solver='liblinear', multi_class='ovr')\n",
    "lrcv_L1.fit(x_train, y_train)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: array([[-0.56429048, -0.56625852, -0.56692688, -0.5670106 ],\n",
       "        [-0.45459863, -0.40728077, -0.40153872, -0.40095335],\n",
       "        [-0.63332645, -0.66369906, -0.66692824, -0.66725411],\n",
       "        [-0.80981201, -0.84786641, -0.85089638, -0.85121256],\n",
       "        [-0.72451712, -0.77437062, -0.78078527, -0.7814305 ],\n",
       "        [-0.24144086, -0.20594123, -0.20230704, -0.20194205],\n",
       "        [-0.35986171, -0.3372109 , -0.33283163, -0.33241186],\n",
       "        [-0.34661177, -0.32315202, -0.3201349 , -0.31984199],\n",
       "        [-0.44726861, -0.44081117, -0.43922835, -0.43907306],\n",
       "        [-0.40005621, -0.38238243, -0.38196027, -0.38191875],\n",
       "        [-1.00593384, -1.06762338, -1.07511874, -1.07591035],\n",
       "        [-0.61876022, -0.65441305, -0.65991425, -0.66047182],\n",
       "        [-0.42926694, -0.40440726, -0.40162944, -0.40135623],\n",
       "        [-0.26984103, -0.23279892, -0.22936205, -0.22902157],\n",
       "        [-0.62114161, -0.59788436, -0.59285088, -0.59234348],\n",
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       "        [-0.55357154, -0.55930486, -0.56078067, -0.56093849],\n",
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       "        [-0.73648359, -0.75618069, -0.75869891, -0.75896681],\n",
       "        [-0.58180111, -0.58763768, -0.58823363, -0.58829481],\n",
       "        [-0.6057071 , -0.6162526 , -0.61853977, -0.61877141],\n",
       "        [-0.51865554, -0.53475331, -0.5370428 , -0.53728714],\n",
       "        [-0.50582351, -0.50323503, -0.50271398, -0.50266332],\n",
       "        [-0.53161865, -0.53790971, -0.53827542, -0.53832538],\n",
       "        [-0.55820192, -0.56382776, -0.56576007, -0.56595696],\n",
       "        [-0.33374368, -0.27713855, -0.27094525, -0.2703621 ],\n",
       "        [-0.26258156, -0.22085112, -0.21690148, -0.21651097],\n",
       "        [-0.33759252, -0.3041446 , -0.30041898, -0.30005128],\n",
       "        [-0.33382477, -0.30818848, -0.30499694, -0.30464464],\n",
       "        [-0.64099925, -0.65647739, -0.65814382, -0.65832727],\n",
       "        [-0.25280479, -0.20367665, -0.19882582, -0.19837129],\n",
       "        [-0.58045633, -0.65816782, -0.66914054, -0.67026304],\n",
       "        [-0.49835651, -0.48734428, -0.48893013, -0.4891111 ],\n",
       "        [-0.47558089, -0.44960278, -0.44660975, -0.4463085 ],\n",
       "        [-0.24335655, -0.24105043, -0.24028782, -0.24022066],\n",
       "        [-0.44287328, -0.4276394 , -0.4259311 , -0.42577927],\n",
       "        [-0.31914861, -0.27450303, -0.27069022, -0.27031293]])}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrcv_L1.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.56429048 -0.56625852 -0.56692688 -0.5670106 ]\n",
      "[-0.45459863 -0.40728077 -0.40153872 -0.40095335]\n",
      "[-0.63332645 -0.66369906 -0.66692824 -0.66725411]\n",
      "[-0.80981201 -0.84786641 -0.85089638 -0.85121256]\n",
      "[-0.72451712 -0.77437062 -0.78078527 -0.7814305 ]\n"
     ]
    },
    {
     "data": {
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Ox8wy5kRinSoimPGHSg5yua9Zr+FEYp3q3udX8uRLr3HleyYyeF+X+5r1Bk4k1mm21yez\n+x4xchAXvdWz+5r1FpkmEknTJFVLqpF0dRttLpBUKWmhpNtb7BssaZmk7+dsu1jSAknPSbovXUHR\nisBP/ryEunXbuPacyZT0UaHDMbMuklkikVQCzATOAiYDF0ua3KJNGXANMDUijgQ+3eIw1wGP5LTv\nC9wMnB4RxwDPkazCaAW2auN2Zj5Uw3smj+BtLvc161WyHJFMAWoiojYidgJ3AOe1aPNxYGZErAOI\niFXNOySdCIwA/pjTXunPAEkCBgPLs+uC5etbc6upb2zy7L5mvVCWiWQ0sDTneV26LddEYKKkxyXN\nkzQNQFIf4AbgqtzGEVEPfBJYQJJAJuM12wtuQd0Gfv10HR+dOp5xLvc163WyTCStXSSPFs/7AmXA\nacDFwC2ShgCXA3MiIjcRIakfSSI5HjiE5NLWNa2+uTRdUoWkitWrV3ekH7YbEcGMuxdy0P79+dd3\nTSh0OGZWAH0zPHYdkDtT3xjefBmqDpiXjjSWSKomSSynAKdKuhwYCPSXtBn4DUBEvAgg6U6g1Zv4\nETELmAVQXl7eMoFZJ7lnwQrmv7SO/3r/0S73NeulshyRzAfKJI2X1B+4CJjdos1dwOkAafXVRKA2\nIi6JiNKIGAd8FrgtIq4GlgGTJTWv1XomUJVhH2w3ttc38l9zFjFp1GAuKPfsvma9VWYjkohokHQF\nMBcoAW6NiIWSZgAVETE73fceSZVAI3BVRKzdzTGXS/oq8KikeuBl4LKs+mC7d8tjtSxbv41vffAY\nl/ua9WKK6PlXfcrLy6OioqLQYfQor27czunffphTy4byow+VFzocM8uApKciot1/4Hld2pI0VdKA\n9PGlkm6UdGhHg7Tu65v3VdPQGC73NbO875H8ANgq6VjgcySXlG7LLCoras/Vrec3T9fxkbeP49CD\nXe5r1tvlm0gaIrkGdh5wc0TcDAzKLiwrVs2z+w4d2J8rTne5r5nln0g2SboGuBS4J53+xLWevdDd\nz62g4uV1fPY9b2GQy33NjPwTyYXADuCfI2IlyTfUv5VZVFaUttc3cv29i5g8ajAfdLmvmaXyLf/d\nRHJJq1HSROAI4JfZhWXF6MePJuW+N1xwrMt9zWyXfEckjwL7SBoNPAB8BPhpVkFZ8Vm5YTv//fCL\nnHXUSE4+7OBCh2NmRSTfRKKI2Aq8H/heRPwDcGR2YVmx+ebcRTQ2Bdec5XJfM3ujvBOJpFOAS4B7\n0m0l2YRkxeaZpev57dPL+OdTx1N68P6FDsfMiky+ieTTJLPs/i6d5uQw4KHswrJikZT7LmTowH34\nV5f7mlkr8rrZHhGPAI9IGiRpYETUAv+WbWhWDGY/u5ynX1nPNz9wDAP3yXKyaDPrrvKdIuVoSX8D\nngcqJT0lyfdIerhtOxv5xr2LOPKQwXzgxDGFDsfMilS+l7Z+BFwZEYdGRCnw78CPswvLisGsR2tZ\nvmE7154z2eW+ZtamfBPJgIjYdU8kIh4GPMlSD7ZiwzZ++MiLvPfokZzkcl8z2418L3rXSvoy8PP0\n+aXAkmxCsmLwzfuqaQyX+5pZ+/IdkXwUGAb8Fvhd+vgjWQVlhfW3V9bxu78t42NvH8/Yg1zua2a7\nl1ciiYh1EfFvEXFCRBwfEZ+KiHXtvU7SNEnVkmoktbq2uqQLJFVKWijp9hb7BktaJun7Odv6S5ol\nabGkRZI+kE8fLD8RwYy7Kxk2aB8ud7mvmeVht5e2JP0BaHMJxYg4dzevLQFmkqyrXgfMlzQ7Iipz\n2pSRfD9lakSskzS8xWGuAx5pse2LwKqImCipD3DQ7vpge2b2s8v52yvr+eb5Lvc1s/y0d6b4dgeO\nPQWoSb9zgqQ7SNYzqcxp83FgZvPoJiJWNe+QdCIwArgPyF3q8aMkk0YSEU3Amg7EaDm27mzg+nsX\ncdTowZx/gst9zSw/u00k6RcR99ZoYGnO8zrgpBZtJgJIepxkypWvRMR96UjjBuBDwLubG0sakj68\nTtJpwIvAFRHxass3lzQdmA5QWlragW70HrMerWXFhu3cfNHx9HG5r5nlKd8vJC6Q9FyLn8ckfUdS\nW7WhrZ2JWl4m6wuUAacBFwO3pMnicmBORCxtpf0Y4PGIOAH4K22MmiJiVkSUR0T5sGHD8ulmr7Z8\nfVLue/Yxo5gy3lcLzSx/+V4EvxdoBJpvhl9Ekig2kEwn/75WXlMH5K5+NAZY3kqbeRFRDyyRVE2S\nWE4BTpV0OTAQ6C9pM8n9lK0klWMA/wf8c559sN345n2LaAq4etoRhQ7FzLqZfBPJ1IiYmvN8gaTH\nI2KqpEvbeM18oEzSeGAZSfL5xxZt7iIZifxU0lCSS121EXFJcwNJlwHlEXF1+vwPJCOYB0kue1Vi\nHfL0K+u465nlXHH6BJf7mtkey/d7JAMl7bq/IWkKyUgBoKG1F0REA3AFMBeoAu5MZw6eIam52msu\nsFZSJclswldFxNp2Yvk88BVJz5HcQ/n3PPtgrWhqCmb8oZLhg/bhk6cdXuhwzKwbUkSb1b2vN5Le\nCtxKkjwEbCS5pFQJnB0Rd2YZZEeVl5dHRUVFocMoSr/7Wx2f+dWzfPuDx3K+J2Y0sxySnoqI8vba\n5TuN/HzgaEkHkCSf9Tm7izqJWNu27mzgG/dWc8yYA3j/8aMLHY6ZdVP5Vm0dIOlGkvXa75d0Q5pU\nrBv74SO1rNyYzO7rcl8z21v53iO5FdgEXJD+bAT+J6ugLHvL1m/jR4+8yDnHjKJ8nMt9zWzv5Vu1\ndXhE5M5p9VVJz2QRkHWNb9y7CICrz3K5r5l1TL4jkm2S3t78RNJUYFs2IVnWnnr5NWY/u5zp7ziM\nMQe63NfMOibfEckngZ8132wHXgMuyyooy05zue+IwfvwL+90ua+ZdVy+VVvPAMdKGpw+35hpVJaZ\nu55ZxrN1G7jhg8cywLP7mlknaG8a+Svb2A5ARNyYQUyWkS07GvjGfYs4dswB/IPLfc2sk7T3J+mg\nLonCusSPHnmRVzfu4L8vOcHlvmbWadqbRv6rXRWIZatu3VZ+9Ggt5x57CCce6nJfM+s8+VZt7SLp\n6SwCsWx9475qJPi8y33NrJPtcSKh9XVGrIhVvPQaf3h2OdPfcTijh+xX6HDMrIfZm0RyT6dHYZlp\nagpm3F3JyMH78i/vPKzQ4ZhZD7THiSQivpRFIJaN3/5tGc/VbeDzZ72F/fu73NfMOl9eZxZJm3jz\nMrkbgArg3yOitrMDs47bsqOBb963iGPHDuG8Y13ua2bZyHdEciNwFTCaZMnczwI/Bu4gmdCxVZKm\nSaqWVCPp6jbaXCCpUtJCSbe32DdY0jJJ32/ldbMlPZ9n/L3SDx5+kVWbdvAf7/PsvmaWnXyvdUyL\niJNyns+SNC8iZkj6QmsvkFQCzATOJFmbfb6k2RFRmdOmjGQd9qkRsU7S8BaHuQ54pJVjvx/YnGfs\nvVLduq3MeqyW8447hBNKDyx0OGbWg+U7ImlKRw590p8Lcva1tcTiFKAmImojYifJ6OW8Fm0+DsyM\niHUAEbGqeYekE4ERwB9zXyBpIHAl8LU8Y++V/uveRfQRfH6ay33NLFv5JpJLSNZHXwW8mj6+VNJ+\nJOuyt2Y0sDTneV26LddEYKKkxyXNkzQNQFIf4AaSy2ktXZfu25pn7L3O/Jde457nVvCJdxzOIS73\nNbOM5TtpYy3wvjZ2/7mN7a1dlG85eukLlAGnkdx7eUzSUcClwJyIWNo8rxeApOOACRHxGUnjdhez\npOnAdIDS0tLdNe1Rmmf3HXXAvp7d18y6RL5VWxOBHwAjIuIoSccA50bE7i4v1QFjc56PAZa30mZe\nRNQDSyRVkySWU4BTJV0ODAT6S9oMvAycKOmlNPbhkh6OiNNavnlEzAJmAZSXl7d1+a3H+c3TdSxY\ntoGbLjyO/fqXFDocM+sF8r209WOSm+L1ABHxHHBRO6+ZD5RJGi+pf9p+dos2dwGnA0gaSnKpqzYi\nLomI0ogYR1IhdltEXB0RP4iIQ9LtbwcWt5ZEeqvNOxr45txqji8dwnnHHVLocMysl8g3kewfEU+2\n2NawuxdERAPJ/ZO5QBVwZ0QslDRD0rlps7nAWkmVwEPAVRGxNv/wLdcPHq5h9aYdXHvOZHIvCZqZ\nZSnf8t81kg4nvcch6XxgRXsviog5wJwW267NeRwkFVitrnuStvkp8NNWtr8EHJVP8L3B0te28uPH\nlvAPx4/meJf7mlkXyjeR/CvJ/YYjJC0DlpBUclmRuP7eRZRIfG7aWwodipn1MvkmkmXA/5BcfjoI\n2Ah8GJiRUVy2B56oXcs9C1bwmTMmMuoAl/uaWdfKN5H8HlgPPM2bK6+sgBrT2X0POWBfpr/Ds/ua\nWdfLN5GMiYhpmUZie+U3T9WxcPlGbr7I5b5mVhj5Vm39RdLRmUZie6y53PeE0iGce6zLfc2sMPId\nkbwduEzSEmAHybfWIyKOySwya9fMh2pYs3kHt3y43OW+ZlYw+SaSszKNwvbY0te28pPHlvD+40dz\n3NghhQ7HzHqxfOfaejnrQGzPfH1OFSV9xOc8u6+ZFdjerNluBTavdi33Pr+ST552OCMP2LfQ4ZhZ\nL+dE0s00prP7jh6yn8t9zawoOJF0M79+aimVKzZy9VlHsG8/l/uaWeE5kXQjm7bX86251ZQfeiDn\nHDOq0OGYmQFOJN3KzIdeZM3mnVz7Ps/ua2bFw4mkm3h57RZu/fMSPnDCGI4Z43JfMyseTiTdxH/N\nWUTfEs/ua2bFJ9NEImmapGpJNZKubqPNBZIqJS2UdHuLfYMlLZP0/fT5/pLukbQobX99lvEXi7++\nuJb7Fq7k8tMOZ8Rgl/uaWXHJ95vte0xSCTATOJNkbfb5kmZHRGVOmzKSJXynRsQ6ScNbHOY64JEW\n274dEQ+ly/c+IOmsiLg3q34UWvPsvqOH7MfHTnW5r5kVnyxHJFOAmoiojYidwB3AeS3afByYGRHr\nACJiVfMOSScCI4A/Nm+LiK0R8VD6eCfJtPZjMuxDwd1ZsZSqFRu55r0u9zWz4pRlIhkNLM15Xpdu\nyzURmCjpcUnzJE0DkNQHuAG4qq2DSxoCvA94oFOjLiIbt9fz7bnVvHXcgZx9tMt9zaw4ZXZpi2SG\n4JailfcvA04jGVk8Juko4FJgTkQsba3MVVJf4JfAdyOittU3l6YD0wFKS0v3sguFNfPBGl7bupOf\nnjPF5b5mVrSyTCR1wNic52N48+qKdcC8iKgHlkiqJkkspwCnSrocGAj0l7Q5Ippv2M8CXoiIm9p6\n84iYlbajvLy8ZQIrei+t2cKtjy/h/BPGcPSYAwodjplZm7K8tDUfKJM0Pr0xfhEwu0Wbu4DTASQN\nJbnUVRsRl0REaUSMAz4L3NacRCR9DTgA+HSGsRfc1+dU0b+kD1f9nct9zay4ZZZIIqIBuAKYC1QB\nd0bEQkkzJJ2bNpsLrJVUCTwEXBURa9s6pqQxwBeBycDTkp6R9LGs+lAof6lZwx8rX+Xy0ycw3OW+\nZlbkFNHtrvrssfLy8qioqCh0GHlpbArO/u5jbN7RwP1XvtOVWmZWMJKeiojy9tr5m+1F5lfzl7Jo\n5Sa+8N5JTiJm1i04kRSRjdvrueGP1UwZdxBnHTWy0OGYmeUly6ot20PfT8t9f+bZfc2sG/GIpEgs\nWbOF/3l8CR88cQxHjXa5r5l1H04kRaK53PezLvc1s27GiaQIPF6zhj9Vvsq/vmsCwwe53NfMuhcn\nkgJraGxixh8qGXvQfnx06vhCh2NmtsecSArsjvlLqX51E184y+W+ZtY9OZEU0IZt9dz4p8WcNP4g\nprnc18y6KSeSAvreAy+wbutOvnyOy33NrPtyIimQ2tWb+elfXuLC8rEu9zWzbs2JpEC+PqeKffuV\n8O/vcbmvmXVvTiQF8NgLq7m/ahVXvGsCwwbtU+hwzMw6xImkizU0NnHd3ZWUHrQ/H5k6rtDhmJl1\nmBNJF/vl/KUsfnUzX3jvJPbp63JfM+v+nEi60Iat9dz4x2pOPuwg/u7IEYUOx8ysU2SaSCRNk1Qt\nqUbS1W20uUBSpaSFkm5vsW+wpGWSvp+z7URJC9JjflfdqG72uw++wPpt9Vx7zpEu9zWzHiOzRCKp\nBJgJnEWyNO7Fkia3aFMGXANMjYgjefM67NcBj7TY9gNgOlCW/kzr/Og734urN/Ozv7zERW8dy+RD\nBhc6HDOzTpPliGQKUBMRtREDLVeaAAAL60lEQVSxE7gDOK9Fm48DMyNiHUBErGreIelEYATwx5xt\no4DBEfHXSNYIvg34+wz70Gm+fo/Lfc2sZ8oykYwGluY8r0u35ZoITJT0uKR5kqYBSOoD3ABc1cox\n69o5JukxpkuqkFSxevXqDnSj4x5dvJoHFq3i/71rAkMHutzXzHqWLBNJazcBosXzviSXp04DLgZu\nkTQEuByYExFLW7TP55jJxohZEVEeEeXDhg3bo8A7U3O576EH789lLvc1sx4oy6V264CxOc/HAMtb\naTMvIuqBJZKqSRLLKcCpki4HBgL9JW0Gbk6Ps7tjFpXbn3yFF1Zt5kcfOtHlvmbWI2U5IpkPlEka\nL6k/cBEwu0Wbu4DTASQNJbnUVRsRl0REaUSMAz4L3BYRV0fECmCTpJPTaq1/An6fYR86ZMPWZHbf\ntx1+MO+Z7HJfM+uZMkskEdEAXAHMBaqAOyNioaQZks5Nm80F1kqqBB4CroqIte0c+pPALUAN8CJw\nbyYd6AQ3PbCYjdvqPbuvmfVoSoqferby8vKoqKjo0vesWbWZaTc9ygVvHcvX/+HoLn1vM7POIOmp\niChvr52/2Z6R/7ynkv36lXDlmRMLHYqZWaacSDLwcPUqHqpezb+9u8zlvmbW4zmRdLL6xia+dk8V\n4w7enw+/bVyhwzEzy5wTSSe7/YlXqFm1mS+ePZn+ff2f18x6Pp/pOtH6rTv5zv2LmTrhYM6YNLzQ\n4ZiZdQknkk500/0vuNzXzHodJ5JOUrNqEz+f9zIXTynliJGe3dfMeg8nkk7ytXuq2L+/y33NrPdx\nIukED1Wv4uHq1Xzq3WUc7HJfM+tlnEg6qL6xia/dXcn4oQP4p1PGFTocM7Mu50TSQf8772VeXL2F\nL753kst9zaxX8pmvA9Zt2clN97/AqWVDebfLfc2sl3Ii6YCb7l/Mpu31fOlsl/uaWe/lRLKXXnh1\nE//7xCtcctKhvGXkoEKHY2ZWME4keyEiuO6eKgb0L+EzLvc1s14u00QiaZqkakk1kq5uo80Fkiol\nLZR0e7rtUElPSXom3f4vOe0vlrRA0nOS7ktXVuxSD1ev5tHFq/nUGRM5aED/rn57M7Oiktma7ZJK\ngJnAmSRrs8+XNDsiKnPalAHXAFMjYp2k5jvWK4C3RcQOSQOB5yXNBlaRrNs+OSLWSPomySqMX8mq\nHy3VNzZx3T2VHDZ0AB86+dCuelszs6KV5YhkClATEbURsRO4AzivRZuPAzMjYh1ARKxKf++MiB1p\nm31y4lT6MyBds30wsDzDPrzJz//6MrWrt/Clc1zua2YG2SaS0cDSnOd16bZcE4GJkh6XNE/StOYd\nksZKei49xjciYnlE1JOs2b6AJIFMBn6SYR/e4LUtO7np/sWcWjaU09/icl8zM8g2kbRWD9tygfi+\nQBlwGnAxcIukIQARsTQijgEmAB+WNEJSP5JEcjxwCPAcyaWxN7+5NF1ShaSK1atXd0Z/uOn+xWzZ\n2ejZfc3McmSZSOqAsTnPx/Dmy1B1wO8joj4ilgDVJIlll4hYDiwETgWOS7e9GBEB3Am8rbU3j4hZ\nEVEeEeXDhg3rcGcWv7qJXzzxCpecVMrEES73NTNrlmUimQ+USRovqT9wETC7RZu7gNMB0uqriUCt\npDGS9ku3HwhMJUkyy4DJkpozw5lAVYZ9ANJy37srk3LfM1zua2aWK7OqrYhokHQFMBcoAW6NiIWS\nZgAVETE73fceSZVAI3BVRKyVdCZwg6QguUT27YhYACDpq8CjkuqBl4HLsupDswcXreKxF9Zw7TmT\nOdDlvmZmb6DkClHPVl5eHhUVFXv12p0NTUy76VEQzP30O+hX4kotM+sdJD0VEeXttfNZsR23/fUl\natds4ctnT3YSMTNrhc+Mu/Halp3c/MALvHPiME4/wuW+ZmatcSLZjRv/VM3WnY186exJhQ7FzKxo\nOZHsxtgD9+cT7ziMMpf7mpm1KbOqrZ7gE+88vNAhmJkVPY9IzMysQ5xIzMysQ5xIzMysQ5xIzMys\nQ5xIzMysQ5xIzMysQ5xIzMysQ5xIzMysQ3rF7L+SVpNMOb83hgJrOjGcQuopfekp/QD3pVj1lL50\ntB+HRkS7KwP2ikTSEZIq8plGuTvoKX3pKf0A96VY9ZS+dFU/fGnLzMw6xInEzMw6xImkfbMKHUAn\n6il96Sn9APelWPWUvnRJP3yPxMzMOsQjEjMz6xAnkpSkaZKqJdVIurqV/ftI+lW6/wlJ47o+yvbl\n0Y/LJK2W9Ez687FCxJkPSbdKWiXp+Tb2S9J3074+J+mEro4xH3n04zRJG3I+k2u7OsZ8SRor6SFJ\nVZIWSvpUK22K/nPJsx/d4nORtK+kJyU9m/blq620yfb8FRG9/gcoAV4EDgP6A88Ck1u0uRz4Yfr4\nIuBXhY57L/txGfD9QseaZ3/eAZwAPN/G/vcC9wICTgaeKHTMe9mP04C7Cx1nnn0ZBZyQPh4ELG7l\n/7Gi/1zy7Ee3+FzS/84D08f9gCeAk1u0yfT85RFJYgpQExG1EbETuAM4r0Wb84CfpY9/Dbxbkrow\nxnzk049uIyIeBV7bTZPzgNsiMQ8YImlU10SXvzz60W1ExIqIeDp9vAmoAka3aFb0n0ue/egW0v/O\nm9On/dKflje/Mz1/OZEkRgNLc57X8eb/qXa1iYgGYANwcJdEl798+gHwgfSSw68lje2a0DKRb3+7\ng1PSSxP3Sjqy0MHkI708cjzJX8C5utXnspt+QDf5XCSVSHoGWAX8KSLa/EyyOH85kSRay8wtM3o+\nbQotnxj/AIyLiGOA+3n9r5TuqDt8Jvl4mmQqimOB7wF3FTiedkkaCPwG+HREbGy5u5WXFOXn0k4/\nus3nEhGNEXEcMAaYIumoFk0y/UycSBJ1QO5f5mOA5W21kdQXOIDiu1zRbj8iYm1E7Eif/hg4sYti\ny0I+n1vRi4iNzZcmImIO0E/S0AKH1SZJ/UhOvr+IiN+20qRbfC7t9aO7fS4AEbEeeBiY1mJXpucv\nJ5LEfKBM0nhJ/UluRs1u0WY28OH08fnAg5HeuSoi7fajxbXqc0muDXdXs4F/SquETgY2RMSKQge1\npySNbL5eLWkKyb/LtYWNqnVpnD8BqiLixjaaFf3nkk8/usvnImmYpCHp4/2AM4BFLZplev7q21kH\n6s4iokHSFcBcksqnWyNioaQZQEVEzCb5n+7nkmpIMvlFhYu4dXn2498knQs0kPTjsoIF3A5JvySp\nnBkqqQ74D5IbiUTED4E5JBVCNcBW4COFiXT38ujH+cAnJTUA24CLivCPlGZTgQ8BC9Jr8gBfAEqh\nW30u+fSju3wuo4CfSSohSXZ3RsTdXXn+8jfbzcysQ3xpy8zMOsSJxMzMOsSJxMzMOsSJxMzMOsSJ\nxMzMOsSJxKwTSNrcfqvdvv7Xkg5LHw+U9CNJL6azuT4q6SRJ/dPHLtu3ouJEYlZg6RxOJRFRm266\nhaTWvywijiT5rs/QdCLOB4ALCxKoWRucSMw6Ufpt7m9Jel7SAkkXptv7SPrvdIRxt6Q5ks5PX3YJ\n8Pu03eHAScCXIqIJIJ3N+Z607V1pe7Oi4SGyWed6P3AccCwwFJgv6VGSb1KPA44GhpNMTXNr+pqp\nwC/Tx0cCz0REYxvHfx54ayaRm+0lj0jMOtfbgV+ms7G+CjxCcuJ/O/B/EdEUESuBh3JeMwpYnc/B\n0wSzU9KgTo7bbK85kZh1rrYWC9rdIkLbgH3TxwuBYyXt7t/mPsD2vYjNLBNOJGad61HgwnShoWEk\ny+w+CfyZZEGxPpJGkEzi2KwKmAAQES8CFcBXc2aeLZN0Xvr4YGB1RNR3VYfM2uNEYta5fgc8BzwL\nPAh8Lr2U9RuSNSGeB35EshrfhvQ19/DGxPIxYCRQI2kByboxzet5nE4yu65Z0fDsv2ZdRNLAiNic\njiqeBKZGxMp0DYmH0udt3WRvPsZvgWsioroLQjbLi6u2zLrO3ekCRP2B69KRChGxTdJ/kKyr/Upb\nL04XK7vLScSKjUckZmbWIb5HYmZmHeJEYmZmHeJEYmZmHeJEYmZmHeJEYmZmHeJEYmZmHfL/AVaB\nPsi6lMCAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b46438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "n_Cs = len(Cs)\n",
    "n_classes = 5\n",
    "array = lrcv_L1.scores_\n",
    "scores =  np.zeros((n_classes,n_Cs))\n",
    "for j in range(n_classes):\n",
    "    print tuple(array.values())[0][j]\n",
    "    scores[j][:] = tuple(array.values())[0][j]\n",
    "    \n",
    "mse_mean = -np.mean(scores, axis = 0)\n",
    "pyplot.plot(np.log10(Cs), mse_mean.reshape(n_Cs,1)) \n",
    "#plt.plot(np.log10(reg.Cs)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "pyplot.xlabel('log(C)')\n",
    "pyplot.ylabel('neg-logloss')\n",
    "pyplot.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.55238723,  7.26068448, -1.06899757, -0.37515867, -0.18397149,\n",
       "         6.07129548,  3.07622922,  1.33518893]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrcv_L1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=[1, 10, 100, 1000], class_weight=None, cv=100,\n",
       "           dual=False, fit_intercept=True, intercept_scaling=1.0,\n",
       "           max_iter=100, multi_class='ovr', n_jobs=1, penalty='l2',\n",
       "           random_state=None, refit=True, scoring='neg_log_loss',\n",
       "           solver='liblinear', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "Cs = [1, 10,100,1000]\n",
    "\n",
    "lr_cv_L2 = LogisticRegressionCV(Cs=Cs, cv = 100, scoring='neg_log_loss', penalty='l2', solver='liblinear', multi_class='ovr')\n",
    "lr_cv_L2.fit(x_train, y_train)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: array([[-0.5782106 , -0.56163941, -0.56612409, -0.56694217],\n",
       "        [-0.46962048, -0.41368526, -0.40236482, -0.40104252],\n",
       "        [-0.65186035, -0.65906224, -0.66609317, -0.66714385],\n",
       "        [-0.75159113, -0.82470819, -0.84790797, -0.85092791],\n",
       "        [-0.64229015, -0.74016371, -0.77632169, -0.78104592],\n",
       "        [-0.33137976, -0.22517311, -0.20451366, -0.20213444],\n",
       "        [-0.44780944, -0.35504782, -0.33488562, -0.33255046],\n",
       "        [-0.43488434, -0.33892148, -0.32184414, -0.31998485],\n",
       "        [-0.51880261, -0.45026336, -0.4402058 , -0.43916642],\n",
       "        [-0.43812255, -0.38725695, -0.38237882, -0.38198219],\n",
       "        [-0.79955182, -1.00541721, -1.06725514, -1.07514129],\n",
       "        [-0.61541176, -0.64235327, -0.65806781, -0.66028182],\n",
       "        [-0.51442874, -0.42238611, -0.40368876, -0.401548  ],\n",
       "        [-0.35920118, -0.25265239, -0.23169205, -0.22923313],\n",
       "        [-0.67859652, -0.61280628, -0.59474001, -0.59252929],\n",
       "        [-0.72785324, -0.86602085, -0.9153074 , -0.92181624],\n",
       "        [-0.42654127, -0.36844661, -0.36229641, -0.36180092],\n",
       "        [-0.59072727, -0.72076003, -0.78004199, -0.78808133],\n",
       "        [-0.48866326, -0.32434595, -0.28549139, -0.28093087],\n",
       "        [-0.55970705, -0.6157883 , -0.64015575, -0.64345877],\n",
       "        [-0.53387363, -0.50030327, -0.49236915, -0.49141451],\n",
       "        [-0.59169264, -0.59207051, -0.59767884, -0.59855506],\n",
       "        [-0.35861694, -0.23515986, -0.21278682, -0.21029254],\n",
       "        [-0.77192851, -0.86383086, -0.89098309, -0.89447211],\n",
       "        [-0.63850849, -0.61121943, -0.60703534, -0.60669442],\n",
       "        [-0.26689325, -0.15353051, -0.13184333, -0.12936469],\n",
       "        [-0.36810343, -0.27706992, -0.26185079, -0.26016433],\n",
       "        [-0.3251341 , -0.2847907 , -0.28075591, -0.28037905],\n",
       "        [-0.48982087, -0.43761009, -0.42861157, -0.42763105],\n",
       "        [-0.48728249, -0.43644297, -0.42665606, -0.42553767],\n",
       "        [-0.56158638, -0.55871342, -0.56880443, -0.57034852],\n",
       "        [-0.3545431 , -0.26922441, -0.25147569, -0.24938452],\n",
       "        [-0.67618542, -0.70788317, -0.71631555, -0.71737737],\n",
       "        [-0.36707593, -0.29497414, -0.28145792, -0.2799098 ],\n",
       "        [-0.78658694, -0.82479661, -0.83577529, -0.837228  ],\n",
       "        [-0.31281686, -0.2270549 , -0.21324687, -0.21174068],\n",
       "        [-0.53786742, -0.51291174, -0.51070439, -0.51055122],\n",
       "        [-0.52320206, -0.715547  , -0.79597787, -0.80671569],\n",
       "        [-0.41412586, -0.36134462, -0.36001714, -0.36025545],\n",
       "        [-0.46935094, -0.34019155, -0.3059781 , -0.30186044],\n",
       "        [-0.38757768, -0.33531649, -0.32814919, -0.32740509],\n",
       "        [-0.71591128, -0.90305536, -0.9695569 , -0.97835906],\n",
       "        [-0.59547628, -0.55111027, -0.54219649, -0.54122277],\n",
       "        [-0.41126519, -0.34468832, -0.32933192, -0.32748244],\n",
       "        [-0.4399403 , -0.38256649, -0.37235566, -0.37126112],\n",
       "        [-0.45518738, -0.43032981, -0.43140805, -0.43172653],\n",
       "        [-0.91331765, -1.16674722, -1.23857228, -1.24764012],\n",
       "        [-0.50109056, -0.49047117, -0.49629198, -0.49722343],\n",
       "        [-0.26361259, -0.19016415, -0.17546823, -0.17373701],\n",
       "        [-0.26929994, -0.17273871, -0.1535991 , -0.15137175],\n",
       "        [-0.44244338, -0.46146489, -0.48388771, -0.48714091],\n",
       "        [-0.43945816, -0.38537469, -0.38189545, -0.38173441],\n",
       "        [-0.29096975, -0.20534552, -0.19022368, -0.18850108],\n",
       "        [-0.62045743, -0.70123278, -0.7342301 , -0.73863247],\n",
       "        [-0.51994428, -0.55496578, -0.5792876 , -0.58276366],\n",
       "        [-0.73419974, -0.78185179, -0.79988771, -0.80232879],\n",
       "        [-0.47432756, -0.44573448, -0.44442384, -0.44445039],\n",
       "        [-0.27922974, -0.14649044, -0.12159751, -0.11880291],\n",
       "        [-0.63841116, -0.66246143, -0.6705445 , -0.6716006 ],\n",
       "        [-0.39758223, -0.35865886, -0.35564547, -0.35544816],\n",
       "        [-0.3039778 , -0.22261587, -0.20758538, -0.20586746],\n",
       "        [-0.28712164, -0.14290101, -0.11723718, -0.11440286],\n",
       "        [-0.41518182, -0.36427788, -0.35810042, -0.35752403],\n",
       "        [-0.6758453 , -0.74192493, -0.76323484, -0.76596233],\n",
       "        [-0.29588772, -0.1752245 , -0.15206088, -0.14942389],\n",
       "        [-0.37421133, -0.32528253, -0.31916337, -0.31854451],\n",
       "        [-0.54235501, -0.49246481, -0.47980378, -0.47826967],\n",
       "        [-0.58721393, -0.59069219, -0.59879682, -0.59998469],\n",
       "        [-0.7262237 , -0.83236706, -0.86616474, -0.87058318],\n",
       "        [-0.51420448, -0.51937963, -0.52218891, -0.52254812],\n",
       "        [-0.32946783, -0.24981264, -0.23682337, -0.23538319],\n",
       "        [-0.42953838, -0.3494727 , -0.33228056, -0.33025024],\n",
       "        [-0.62840449, -0.59941145, -0.59233397, -0.59152117],\n",
       "        [-0.41590376, -0.33518209, -0.31799145, -0.31597026],\n",
       "        [-0.41013452, -0.42057319, -0.43901668, -0.44166815],\n",
       "        [-0.29406093, -0.19691362, -0.17891457, -0.17685247],\n",
       "        [-0.36248471, -0.25734083, -0.23793878, -0.23574385],\n",
       "        [-0.52654359, -0.54775568, -0.55923112, -0.56080749],\n",
       "        [-0.54718775, -0.51578639, -0.50806217, -0.50712204],\n",
       "        [-0.3608689 , -0.27309187, -0.2587049 , -0.25715263],\n",
       "        [-0.542608  , -0.58528372, -0.60351806, -0.60595572],\n",
       "        [-0.72876405, -0.75191046, -0.75807287, -0.75889429],\n",
       "        [-0.58837518, -0.58499512, -0.58779307, -0.58826452],\n",
       "        [-0.58867314, -0.60951672, -0.61762237, -0.61868297],\n",
       "        [-0.55326078, -0.53703672, -0.53718988, -0.5373251 ],\n",
       "        [-0.57073246, -0.51517626, -0.50400176, -0.50276352],\n",
       "        [-0.542725  , -0.53540163, -0.53782814, -0.53825885],\n",
       "        [-0.55851089, -0.55962353, -0.56510242, -0.56591596],\n",
       "        [-0.40231094, -0.29718429, -0.27340125, -0.27056985],\n",
       "        [-0.32173052, -0.23598011, -0.21868438, -0.21667778],\n",
       "        [-0.44994237, -0.3309497 , -0.30358482, -0.30033373],\n",
       "        [-0.4224537 , -0.32568729, -0.30695066, -0.30482023],\n",
       "        [-0.62890609, -0.65020028, -0.65729169, -0.65824591],\n",
       "        [-0.35230702, -0.22687004, -0.2015179 , -0.19860123],\n",
       "        [-0.53180549, -0.62005261, -0.66372869, -0.66974775],\n",
       "        [-0.52433588, -0.48844877, -0.48877332, -0.48910613],\n",
       "        [-0.50763194, -0.45725236, -0.44749076, -0.44639517],\n",
       "        [-0.34120738, -0.25609067, -0.2419122 , -0.24034621],\n",
       "        [-0.46827536, -0.43223907, -0.42640476, -0.42578484],\n",
       "        [-0.39884379, -0.29182178, -0.27264962, -0.27049241]])}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_cv_L2.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.56429048 -0.56625852 -0.56692688 -0.5670106 ]\n",
      "[-0.45459863 -0.40728077 -0.40153872 -0.40095335]\n",
      "[-0.63332645 -0.66369906 -0.66692824 -0.66725411]\n",
      "[-0.80981201 -0.84786641 -0.85089638 -0.85121256]\n",
      "[-0.72451712 -0.77437062 -0.78078527 -0.7814305 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ox8wy5kRinSoimPGHSg5yua9Zr+FEYp3q3udX8uRLr3HleyYyeF+X+5r1Bk4k1mm21yez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\nPsi6lMCAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xef2f160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dict with classes as the keys, and the values as the grid of scores obtained during cross-validating each fold,\n",
    "# Each dict value has shape (n_folds, len(Cs))\n",
    "n_Cs = len(Cs)\n",
    "n_classes = 5\n",
    "scores =  np.zeros((n_classes,n_Cs))\n",
    "for j in range(n_classes):\n",
    "    print tuple(array.values())[0][j]\n",
    "    scores[j][:] = tuple(array.values())[0][j]\n",
    "    \n",
    "mse_mean = -np.mean(scores, axis = 0)\n",
    "pyplot.plot(np.log10(Cs), mse_mean.reshape(n_Cs,1)) \n",
    "#plt.plot(np.log10(reg.Cs)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "pyplot.xlabel('log(C)')\n",
    "pyplot.ylabel('neg-logloss')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "SVC1 = LinearSVC().fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for classifier LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0):\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.81      0.87      0.84       105\n",
      "          1       0.67      0.57      0.62        49\n",
      "\n",
      "avg / total       0.77      0.77      0.77       154\n",
      "\n",
      "\n",
      "Confusion matrix:\n",
      "[[91 14]\n",
      " [21 28]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn import metrics\n",
    "y_predict = SVC1.predict(x_test)\n",
    "\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (SVC1, metrics.classification_report(y_test, y_predict)))\n",
    "print(\"Confusion matrix:\\n%s\" % metrics.confusion_matrix(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fit_grid_point_Linear(C, x_train, y_train, x_test, y_test):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC2 =  LinearSVC( C = C)\n",
    "    SVC2 = SVC2.fit(x_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC2.score(x_test, y_test)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.681818181818\n",
      "accuracy: 0.701298701299\n",
      "accuracy: 0.779220779221\n",
      "accuracy: 0.772727272727\n",
      "accuracy: 0.766233766234\n",
      "accuracy: 0.772727272727\n",
      "accuracy: 0.694805194805\n"
     ]
    },
    {
     "data": {
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WTJwIbdokXY1I/KZMgbVrw61SKCzkoC1cGIbNqgUllWLSpPBnZh20SqCwkIOWTkPnznDB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\nZPX/AW07SvYpoadSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf7ae7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C_s = np.logspace(-3, 3, 7)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份  \n",
    "#penalty_s = ['l1','l2']\n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "#    for j, penalty in enumerate(penalty_s):\n",
    "    tmp = fit_grid_point_Linear(oneC, x_train, y_train, x_test, y_test)\n",
    "    accuracy_s.append(tmp)\n",
    "\n",
    "x_axis = np.log10(C_s)\n",
    "#for j, penalty in enumerate(penalty_s):\n",
    "pyplot.plot(x_axis, np.array(accuracy_s), 'b-')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuracy' )\n",
    "pyplot.savefig('SVM_Otto.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "def fit_grid_point_RBF(C, gamma, X_train, y_train, X_val, y_val):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC3 =  SVC( C = C, kernel='rbf', gamma = gamma)\n",
    "    SVC3 = SVC3.fit(x_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC3.score(x_test, y_val)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.766233766234\n",
      "accuracy: 0.707792207792\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.785714285714\n",
      "accuracy: 0.785714285714\n",
      "accuracy: 0.74025974026\n",
      "accuracy: 0.675324675325\n",
      "accuracy: 0.785714285714\n",
      "accuracy: 0.772727272727\n",
      "accuracy: 0.772727272727\n",
      "accuracy: 0.74025974026\n",
      "accuracy: 0.694805194805\n",
      "accuracy: 0.766233766234\n",
      "accuracy: 0.766233766234\n",
      "accuracy: 0.753246753247\n",
      "accuracy: 0.675324675325\n",
      "accuracy: 0.694805194805\n"
     ]
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-2, 2, 5)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份 \n",
    "gamma_s = np.logspace(-2, 2, 5)  \n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, x_train, y_train, x_test, y_test)\n",
    "        accuracy_s.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.785714285714\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.681818181818\n",
      "accuracy: 0.785714285714\n",
      "accuracy: 0.766233766234\n"
     ]
    }
   ],
   "source": [
    "C_s = np.logspace(-1, 2, 4)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份 \n",
    "gamma_s = np.logspace(-5, -2, 4)  \n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, x_train, y_train, x_test, y_test)\n",
    "        accuracy_s.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NRRDGtB9jdZQK4cyRRRLQsMTtBkBZ/+UbBiy7aN0txpgDxpgCYBXQySkpLaYt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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1021e9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_s1 =np.array(accuracy_s).reshape(len(C_s),len(gamma_s))\n",
    "x_axis = np.log10(C_s)\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    pyplot.plot(x_axis, np.array(accuracy_s1[:,j]), label = ' Test - log(gamma)' + str(np.log10(gamma)))\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuracy' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
